{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40b98eb2-2aaa-4e49-8fc0-89b0540ff564",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 105766d2 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (112 CPUs, 503.5 GB RAM, 26.2/30.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "import comet_ml\n",
    "import torch\n",
    "import utils\n",
    "\n",
    "comet_ml.init(project_name='exp_100epoch')\n",
    "# 这里应该会包含100epoch的0,0.6,1.2加雾以及各个以100epoch为单位的增量\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "41ab04bb-e8f4-4f54-9cf1-60ec02dc4652",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_DER: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=der_1, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=[], Lwf_temperature=1.0, Old_models=[], DER_enable=True, DER_old_model=['./runs/train/fog_02/weights/last.pt']\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2895 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 105766d2 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/1d4a530387c945b1b417567865731f89\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_DerTest.Detect              [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "extractors长度： 1\n",
      "首次创建 extractors\n",
      "成功拼接 extractors\n",
      "extractors共有模型个数： 2\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1    166941  models.yolo_DerTest.Detect              [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]]\n",
      "已知类别： 8\n",
      "YOLOv5s_VOCKITTI summary: 441 layers, 14279030 parameters, 7237819 gradients, 81.8 GFLOPs\n",
      "\n",
      "Transferred 342/1078 items from runs/train/fog_02/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 114 weight(decay=0.0), 141 weight(decay=0.0005), 123 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007... 16551 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/train2007.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/der_1/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/der_1\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      6.48G    0.05311    0.03682    0.05372         36        640: 1\n",
      "tensor([1.97605], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.341      0.165      0.133     0.0652\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      9.05G    0.05011    0.03404    0.03509         58        640: 1\n",
      "tensor([2.46344], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.38      0.413      0.364      0.177\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      9.05G    0.04781    0.03461    0.02723         37        640: 1\n",
      "tensor([1.56876], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.47      0.494      0.459      0.233\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      9.05G    0.04437    0.03383    0.02539         46        640: 1\n",
      "tensor([1.56750], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.597      0.535      0.568      0.303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      9.05G    0.04379    0.03395    0.02403         39        640: 1\n",
      "tensor([1.60419], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.627       0.57      0.601      0.334\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      9.05G    0.04192    0.03295    0.02112         28        640: 1\n",
      "tensor([1.30833], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.61       0.58      0.607      0.345\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      9.05G    0.04162     0.0324    0.01974         39        640: 1\n",
      "tensor([1.47403], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.636       0.61       0.64       0.37\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      9.05G    0.04021    0.03223    0.01884         34        640: 1\n",
      "tensor([1.26869], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696      0.633      0.687      0.407\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      9.05G    0.03985    0.03198    0.01831         41        640: 1\n",
      "tensor([1.18205], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.682      0.643      0.693      0.411\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      9.05G    0.03923    0.03113     0.0175         46        640: 1\n",
      "tensor([1.32747], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696      0.639        0.7      0.423\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      9.05G    0.03898    0.03183    0.01729         30        640: 1\n",
      "tensor([1.24200], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.704      0.655      0.715      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      9.05G    0.03752    0.03131    0.01683         26        640: 1\n",
      "tensor([1.15200], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.706      0.659      0.706      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      9.05G    0.03749    0.03117    0.01687         33        640: 1\n",
      "tensor([1.41148], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.669      0.719      0.446\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      9.05G    0.03743     0.0303    0.01473         30        640: 1\n",
      "tensor([1.06897], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.713      0.679      0.731      0.459\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      9.05G    0.03726    0.03033    0.01433         33        640: 1\n",
      "tensor([1.29213], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.716      0.692      0.737      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      9.05G    0.03656    0.02936    0.01419         23        640: 1\n",
      "tensor([0.91881], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.716      0.689       0.74      0.474\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      9.05G    0.03651    0.03067     0.0139         40        640: 1\n",
      "tensor([1.33373], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.733      0.697      0.758      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      9.05G    0.03594    0.03047    0.01338         45        640: 1\n",
      "tensor([1.08797], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.736      0.694      0.755      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      9.05G    0.03507    0.02959    0.01329         20        640: 1\n",
      "tensor([0.72077], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739       0.71       0.77      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      9.05G    0.03499    0.02995    0.01336         38        640: 1\n",
      "tensor([1.30284], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.735      0.711       0.77      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      9.05G    0.03493    0.02921    0.01239         33        640: 1\n",
      "tensor([1.30976], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.748      0.727      0.779      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      9.05G    0.03506    0.02914    0.01223         28        640: 1\n",
      "tensor([1.03469], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.765      0.713      0.779      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      9.05G    0.03418    0.02903    0.01153         27        640: 1\n",
      "tensor([1.03421], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.743      0.738      0.783      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      9.05G    0.03401    0.02843    0.01155         29        640: 1\n",
      "tensor([1.43898], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.734      0.791       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      9.05G    0.03342    0.02804    0.01181         35        640: 1\n",
      "tensor([0.85336], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.732      0.795      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      9.05G    0.03303    0.02862    0.01083         31        640: 1\n",
      "tensor([0.82622], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.737      0.797      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      9.05G    0.03326    0.02788    0.01076         40        640: 1\n",
      "tensor([1.32322], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.746      0.798      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      9.05G    0.03296    0.02841    0.01052         29        640: 1\n",
      "tensor([1.07267], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.747      0.801      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      9.05G    0.03208    0.02756    0.01001         26        640: 1\n",
      "tensor([0.80469], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.738      0.796      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      9.05G    0.03196    0.02769   0.009534         45        640: 1\n",
      "tensor([1.23599], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.753      0.807      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      9.05G    0.03165    0.02741   0.009563         36        640: 1\n",
      "tensor([0.94159], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.759      0.809      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      9.05G    0.03152    0.02605   0.009128         20        640: 1\n",
      "tensor([1.05429], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784       0.75      0.811      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      9.05G    0.03152    0.02622   0.009704         25        640: 1\n",
      "tensor([0.94990], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.757       0.81       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      9.05G    0.03132    0.02681   0.008577         34        640: 1\n",
      "tensor([1.04568], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.756      0.813      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      9.05G    0.03084    0.02737   0.008772         47        640: 1\n",
      "tensor([1.10389], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.776      0.759      0.815      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      9.05G     0.0303    0.02669   0.008219         35        640: 1\n",
      "tensor([0.86789], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783      0.754      0.817       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      9.05G     0.0303    0.02707   0.008297         41        640: 1\n",
      "tensor([0.94986], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.762      0.816      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      9.05G    0.03001    0.02664    0.00798         41        640: 1\n",
      "tensor([1.29302], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.765       0.82      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      9.05G    0.03013    0.02571   0.007791         23        640: 1\n",
      "tensor([1.09120], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.765      0.819      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      9.05G    0.02939    0.02502   0.008023         29        640: 1\n",
      "tensor([0.92138], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783       0.76      0.818      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      9.05G    0.02887    0.02572   0.007191         33        640: 1\n",
      "tensor([0.86441], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.777      0.763      0.821      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      9.05G    0.02838    0.02554     0.0075         33        640: 1\n",
      "tensor([0.91732], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.764      0.821      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      9.05G    0.02827    0.02511   0.007754         34        640: 1\n",
      "tensor([1.08174], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.777      0.769      0.818      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      9.05G    0.02814    0.02507   0.007493         35        640: 1\n",
      "tensor([1.04322], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778       0.77       0.82      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      9.05G    0.02773    0.02516   0.006852         33        640: 1\n",
      "tensor([0.76964], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.788      0.766      0.823      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      9.05G    0.02732    0.02455   0.006795         34        640: 1\n",
      "tensor([1.00818], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.793      0.763      0.824      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      9.05G    0.02709    0.02422   0.006936         40        640: 1\n",
      "tensor([1.20786], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.788      0.771      0.825      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      9.05G    0.02712    0.02485   0.006495         42        640: 1\n",
      "tensor([0.73141], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.792      0.767      0.824      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      9.05G    0.02603    0.02424    0.00641         26        640: 1\n",
      "tensor([0.52743], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.79      0.773      0.824      0.583\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      9.05G    0.02656    0.02351   0.006166         21        640: 1\n",
      "tensor([0.61815], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.79      0.771      0.824      0.583\n",
      "\n",
      "50 epochs completed in 2.929 hours.\n",
      "Optimizer stripped from runs/train/der_1/weights/last.pt, 29.1MB\n",
      "Optimizer stripped from runs/train/der_1/weights/best.pt, 29.1MB\n",
      "\n",
      "Validating runs/train/der_1/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 384 layers, 14269526 parameters, 0 gradients, 81.1 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.79      0.773      0.824      0.583\n",
      "                   car       4952       1201      0.816      0.899      0.929      0.722\n",
      "                person       4952       4528      0.861      0.826      0.895      0.601\n",
      "             aeroplane       4952        285      0.919      0.821      0.905       0.62\n",
      "               bicycle       4952        337      0.916      0.838      0.909      0.643\n",
      "                  bird       4952        459      0.815      0.727      0.807      0.523\n",
      "                  boat       4952        263      0.677      0.684      0.727      0.432\n",
      "                bottle       4952        469      0.692      0.731      0.735        0.5\n",
      "                   bus       4952        213      0.852       0.84      0.883      0.739\n",
      "                   cat       4952        358      0.866      0.818      0.874       0.65\n",
      "                 chair       4952        756      0.655      0.626       0.67      0.446\n",
      "                   cow       4952        244      0.757      0.866      0.872       0.65\n",
      "           diningtable       4952        206      0.749      0.697      0.752      0.514\n",
      "                   dog       4952        489      0.824      0.728      0.855      0.612\n",
      "                 horse       4952        348      0.855      0.862      0.901      0.663\n",
      "             motorbike       4952        325      0.873      0.834      0.898      0.608\n",
      "           pottedplant       4952        480      0.638      0.522       0.55      0.298\n",
      "                 sheep       4952        242      0.729      0.846      0.859      0.635\n",
      "                  sofa       4952        239        0.7      0.682      0.765      0.583\n",
      "                 train       4952        282       0.85       0.83      0.872      0.617\n",
      "             tvmonitor       4952        308      0.762      0.789      0.826      0.601\n",
      "Results saved to \u001b[1mruns/train/der_1\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : der_1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/1d4a530387c945b1b417567865731f89\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8670857665118906\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 21.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.9053507101024555\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.6201695569112032\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9185872787461147\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8210526315789474\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 234.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8751449218415447\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 26.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.9091788971532763\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6426380091886075\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.9156982077694052\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.8380312519778396\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 282.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7683704871255895\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 76.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.8068145003813423\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.5227248599166722\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.8145289144135358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.7271629875589334\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 334.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6803201409874654\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 86.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.7272727465564623\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.4322400618921104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6765895257578475\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6840921244438354\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 180.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.7109467815813494\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 153.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7354739712834262\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.5001873032231917\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6916570915435909\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7313432835820896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 343.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8462357569401018\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 31.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8826159149572278\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7393139876092991\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8521782301030565\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8403755868544601\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 179.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8558771994871306\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 243.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.929285429654866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.7224536013587375\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.816354912492159\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8994209735258861\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1080.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.8414394102514947\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 45.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.873526275483677\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.6502982125574317\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8657735838856783\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.8184357541899442\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 293.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6401988782677224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 249.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6696341868892918\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.4462441768037534\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6554280212806959\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6256613756613757\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 473.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.8077172452877776\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 68.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8724754036236286\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6498976135138891\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7565909585981271\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8662539588769097\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 211.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.7222603146224021\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 48.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7517592666664464\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.5139147228087302\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7494467416805985\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6969772361358122\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 144.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7731085375245252\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 76.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8549322207649838\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.6122673151321109\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8241554156201402\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7280163599182005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 356.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8585683453528756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.90147590293114\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.662575073821724\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8550960402812254\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8620689655172413\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 300.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (1.3064323663711548, 8.183149337768555)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.13317884924490495, 0.8247439089278783)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.06523340884475001, 0.5828943346718427)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.3414417153824277, 0.7933984257672694)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.1645398284998897, 0.7732512116547453)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8528935960588326\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 39.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8984133400840952\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.6076478868675735\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8728315773694884\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.8338461538461538\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 271.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8432646275835639\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 604.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8946074941824006\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.6006859959378803\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8610570994164253\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8261925795053003\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3741.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5746648905325679\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 142.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5498660694125324\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.29848993744622365\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6384715824858803\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.5224527305082861\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 251.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7833148030258404\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 76.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8589589888753528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.6354439320860872\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7292784294207781\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8459997187269914\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 205.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6910321463332458\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 70.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7653480989656092\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.5832944145525001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.70029791684701\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6820083682008368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 163.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02602875605225563, 0.053105678409338)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.006166236940771341, 0.05372410640120506)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.023510854691267014, 0.03682367876172066)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.839965342192839\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 41.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8721866476049707\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.6170638451104177\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8503962388547369\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.8297872340425532\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 234.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7750087297547905\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 76.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.8261575783037891\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.6012182958550769\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7617129889232831\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7887768721102054\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 243.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.02942751906812191, 0.04752011224627495)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.005250735208392143, 0.04357029125094414)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.017377521842718124, 0.022490520030260086)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : der_1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/1d4a530387c945b1b417567865731f89\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/der_1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.03 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 158.15 KB/462.15 KB\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_DER.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--DER_enable \\\n",
    "--DER_old_model \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "--name der_1 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 这是没有数据回放的\n",
    "# 三个小时还是太离谱了一点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e2033270-a100-450a-9938-78f19f09fc60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/der_1/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 105766d2 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 384 layers, 14269526 parameters, 0 gradients, 81.1 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198       0.18      0.143      0.152     0.0801\n",
      "                   car       2244       8711      0.826      0.653      0.745      0.419\n",
      "                   van       2244        861          0          0          0          0\n",
      "                 truck       2244        333          0          0          0          0\n",
      "                  tram       2244        138          0          0          0          0\n",
      "                person       2244       1286      0.611      0.488      0.469      0.221\n",
      "        person_sitting       2244         89          0          0          0          0\n",
      "               cyclist       2244        496          0          0          0          0\n",
      "                  misc       2244        284          0          0          0          0\n",
      "Speed: 0.0ms pre-process, 1.5ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp265\u001b[0m\n",
      "Vis\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/der_1/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Vis' \\\n",
    "\" \n",
    "!{val_command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7921315b-4d4f-44fc-abbb-2b29f72c726b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d35d906-b534-4f99-9b4c-1e395f104944",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59f49d97-b224-4389-be2e-25760017e677",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c1b0a62-3534-476e-9be0-d8af7d2ce2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# VOCKITTIBiC_base是回放数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "093c672c-324f-477d-b78a-95974bfffa5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_DER: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=der_replay, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=[], Lwf_temperature=1.0, Old_models=[], DER_enable=True, DER_old_model=['./runs/train/fog_02/weights/last.pt']\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 105766d2 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/3c808429d10a497c85a0d9ed321dfed7\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_DerTest.Detect              [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "extractors长度： 1\n",
      "首次创建 extractors\n",
      "成功拼接 extractors\n",
      "extractors共有模型个数： 2\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1    166941  models.yolo_DerTest.Detect              [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]]\n",
      "已知类别： 8\n",
      "YOLOv5s_VOCKITTI summary: 441 layers, 14279030 parameters, 7237819 gradients, 81.8 GFLOPs\n",
      "\n",
      "Transferred 342/1078 items from runs/train/fog_02/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 114 weight(decay=0.0), 141 weight(decay=0.0005), 123 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old... 18599 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.17 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/der_replay/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/der_replay\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      6.48G    0.05844    0.04105    0.05519        112        640:  fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "       0/49      6.48G    0.05056    0.03699    0.04642         64        640: 1\n",
      "tensor([2.40620], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.437      0.168      0.138     0.0688\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      9.05G    0.04771    0.03291    0.03102         21        640: 1\n",
      "tensor([1.42770], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.463      0.468      0.406      0.209\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      9.05G    0.04644    0.03376    0.02405         31        640: 1\n",
      "tensor([1.33607], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.506      0.487      0.481      0.243\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      9.05G    0.04385    0.03372    0.02215         29        640: 1\n",
      "tensor([1.21831], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.545      0.526      0.527      0.271\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      9.05G    0.04291    0.03364    0.01936         44        640: 1\n",
      "tensor([1.20008], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.643      0.589      0.627      0.355\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      9.05G    0.04163    0.03336     0.0188         30        640: 1\n",
      "tensor([1.20580], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.628      0.587      0.616       0.34\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      9.05G     0.0407    0.03254    0.01715         44        640: 1\n",
      "tensor([1.22665], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.657      0.597      0.647       0.37\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      9.05G     0.0397    0.03186    0.01655         41        640: 1\n",
      "tensor([1.19485], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.673      0.635      0.681      0.395\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      9.05G    0.03938    0.03178    0.01544         35        640: 1\n",
      "tensor([0.99680], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.693      0.638      0.696      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      9.05G    0.03866    0.03194    0.01528         33        640: 1\n",
      "tensor([1.15737], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.712      0.649      0.708      0.431\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      9.05G    0.03805    0.03139    0.01469         59        640: 1\n",
      "tensor([1.19693], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.698      0.657      0.709      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      9.05G    0.03798     0.0318    0.01454         34        640: 1\n",
      "tensor([1.15875], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.715      0.659      0.721      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      9.05G    0.03753    0.03081    0.01381         56        640: 1\n",
      "tensor([1.47603], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.715      0.682      0.742      0.466\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      9.05G    0.03668    0.03017    0.01276         36        640: 1\n",
      "tensor([1.20417], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.724      0.678       0.74      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      9.05G    0.03704    0.03098    0.01252         69        640: 1\n",
      "tensor([1.61158], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.745      0.687      0.752      0.479\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      9.05G    0.03641    0.03074    0.01231         51        640: 1\n",
      "tensor([1.17140], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.732      0.692      0.752      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      9.05G    0.03599    0.03045    0.01196         43        640: 1\n",
      "tensor([1.27530], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.711      0.763      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      9.05G    0.03611    0.02979     0.0117         27        640: 1\n",
      "tensor([1.08377], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747        0.7      0.761      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      9.05G    0.03514    0.02946    0.01095         33        640: 1\n",
      "tensor([1.04420], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.709      0.771      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      9.05G    0.03574    0.03003    0.01087         46        640: 1\n",
      "tensor([1.23798], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.744      0.719      0.776      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.723      0.776      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      9.05G    0.03536    0.02921    0.01033         29        640: 1\n",
      "tensor([1.13513], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.728      0.782      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      9.05G    0.03407    0.02901    0.01065         35        640: 1\n",
      "tensor([1.05041], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.748      0.726      0.783      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      9.05G     0.0342    0.02904    0.01026         37        640: 1\n",
      "tensor([0.95688], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.725      0.792      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      9.05G    0.03351    0.02834   0.009973         31        640: 1\n",
      "tensor([0.89856], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.758      0.734       0.79      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      9.05G     0.0335     0.0286    0.00966         47        640: 1\n",
      "tensor([1.38229], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.732      0.797      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      9.05G    0.03341    0.02867   0.009736         40        640: 1\n",
      "tensor([0.93903], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753      0.757        0.8      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      9.05G    0.03324    0.02852   0.009031         31        640: 1\n",
      "tensor([1.02152], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.759      0.748      0.797      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      9.05G     0.0328    0.02798   0.008873         26        640: 1\n",
      "tensor([0.72806], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.776      0.745      0.803      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      9.05G    0.03234    0.02727   0.009255         33        640: 1\n",
      "tensor([1.16636], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.758      0.806      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      9.05G    0.03181    0.02798   0.008723         40        640: 1\n",
      "tensor([1.10834], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783       0.74      0.807      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      9.05G    0.03113    0.02738   0.007883         40        640: 1\n",
      "tensor([0.87211], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.746      0.812      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      9.05G    0.03158    0.02767   0.008163         33        640: 1\n",
      "tensor([0.84387], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.761      0.813      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      9.05G    0.03161     0.0275   0.008166         36        640: 1\n",
      "tensor([1.15018], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.779      0.751      0.813      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      9.05G      0.031    0.02737   0.007424         44        640: 1\n",
      "tensor([1.00957], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774       0.76      0.814      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      9.05G    0.03014    0.02698   0.007493         34        640: 1\n",
      "tensor([0.79010], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.777      0.763      0.815      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      9.05G    0.03075    0.02672   0.007492         56        640: 1\n",
      "tensor([1.04779], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.787      0.753      0.817      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      9.05G    0.03002    0.02674   0.006984         44        640: 1\n",
      "tensor([0.91062], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.766      0.817      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      9.05G    0.02973    0.02586   0.007039         34        640: 1\n",
      "tensor([0.78153], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.763      0.817      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      9.05G    0.02943    0.02617    0.00689         48        640: 1\n",
      "tensor([1.09333], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.768      0.818      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      9.05G    0.02934    0.02603   0.006644         56        640: 1\n",
      "tensor([1.05143], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.779      0.765      0.818      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      9.05G    0.02901    0.02595   0.006455         36        640: 1\n",
      "tensor([0.79189], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.761      0.819      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      9.05G      0.029    0.02604    0.00686         41        640: 1\n",
      "tensor([1.03883], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789       0.76      0.821      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      9.05G    0.02819    0.02565   0.006028         38        640: 1\n",
      "tensor([0.73221], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.765      0.822      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      9.05G    0.02817    0.02437   0.006296         21        640: 1\n",
      "tensor([0.59545], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.793      0.764      0.822      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      9.05G    0.02787    0.02486   0.005895         40        640: 1\n",
      "tensor([0.85061], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.764      0.821      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      9.05G    0.02766    0.02494   0.005814         40        640: 1\n",
      "tensor([1.02765], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.788      0.766      0.821      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      9.05G    0.02721     0.0242   0.005545         31        640: 1\n",
      "tensor([0.60222], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.791      0.762      0.821      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      9.05G    0.02672    0.02405   0.005339         26        640: 1\n",
      "tensor([0.63203], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.767      0.821      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      9.05G    0.02706    0.02472   0.005557         30        640: 1\n",
      "tensor([0.78839], device='cuda:0', grad_fn=<AddBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.787      0.771      0.821      0.579\n",
      "\n",
      "50 epochs completed in 3.286 hours.\n",
      "Optimizer stripped from runs/train/der_replay/weights/last.pt, 29.1MB\n",
      "Optimizer stripped from runs/train/der_replay/weights/best.pt, 29.1MB\n",
      "\n",
      "Validating runs/train/der_replay/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 384 layers, 14269526 parameters, 0 gradients, 81.1 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.772      0.821      0.579\n",
      "                   car       4952       1201      0.823      0.893      0.927      0.716\n",
      "                person       4952       4528      0.858      0.824      0.891      0.597\n",
      "             aeroplane       4952        285      0.913      0.805      0.894      0.613\n",
      "               bicycle       4952        337      0.881      0.831      0.911      0.645\n",
      "                  bird       4952        459      0.781      0.745      0.805      0.526\n",
      "                  boat       4952        263      0.628      0.667      0.683      0.401\n",
      "                bottle       4952        469      0.664      0.723      0.732      0.507\n",
      "                   bus       4952        213      0.827       0.85      0.892      0.734\n",
      "                   cat       4952        358      0.868      0.829      0.873      0.645\n",
      "                 chair       4952        756      0.657       0.64       0.68      0.439\n",
      "                   cow       4952        244      0.763      0.861      0.876      0.657\n",
      "           diningtable       4952        206      0.746      0.684      0.752      0.535\n",
      "                   dog       4952        489      0.837      0.746      0.855      0.607\n",
      "                 horse       4952        348      0.879      0.879      0.914       0.67\n",
      "             motorbike       4952        325      0.867      0.801      0.883      0.584\n",
      "           pottedplant       4952        480      0.644      0.525      0.542      0.288\n",
      "                 sheep       4952        242      0.724      0.822      0.861      0.632\n",
      "                  sofa       4952        239      0.716      0.724      0.767      0.582\n",
      "                 train       4952        282      0.845      0.813       0.86      0.602\n",
      "             tvmonitor       4952        308      0.783      0.776      0.821      0.595\n",
      "Results saved to \u001b[1mruns/train/der_replay\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : der_replay\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/3c808429d10a497c85a0d9ed321dfed7\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8555496240920772\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 22.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8936977399141183\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.6125989023340921\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9125252512180988\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8052706842180527\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 230.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8551376166145714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 38.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.9114001207724006\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6453793399763478\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.880876114493161\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.8308605341246291\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 280.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7622983835751708\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 96.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.8046579563763003\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.5255604503284295\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7807334650693794\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.7447138166092415\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 342.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6470557082211474\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 104.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6827959661867062\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.4008068054563626\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6279334007729069\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.667379250818203\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 176.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6922109646598288\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 171.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7316560833031653\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.5073176190719634\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6640936366319122\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7228144989339019\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 339.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8384296566074829\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 38.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8919164854117636\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7343664322523176\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.82739250055822\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8497652582159625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 181.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8567150830151892\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 231.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9269580633468034\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.7163506690059569\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8228612253164574\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8934740686712083\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1073.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.8481992216900681\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 45.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8725484193771883\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.645497636524401\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8683337902353183\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.8289772367984659\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 297.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6483329609385724\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 253.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6796792055659192\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.438757983179335\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6566629728867321\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6402116402116402\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 484.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.8090987890903419\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.875628710252922\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6571413458497254\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7633696895954065\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.860655737704918\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 210.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.7141345399077291\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 48.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7515282563914194\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.5348453850754814\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7464915896288445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6844660194174758\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 141.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7892088376778044\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 71.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8554149989615628\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.6070485367582372\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8371615585102034\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7464519734458385\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 365.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8793915957927311\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.9138574189409918\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6697340319582124\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8793278012722732\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8794553995703421\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 306.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5815]                 : (0.9390252828598022, 8.357418060302734)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.13777889889068404, 0.8218180995360745)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.06878333356652194, 0.5788722775421788)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.4374126393506345, 0.7926054252690156)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.16828672304934106, 0.7711536432790032)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.832384745877212\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 40.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8830051138612918\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5840227868065451\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8667579207114091\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.8006338646338647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 260.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8408149923842705\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 616.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8908146368582293\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5968411169927957\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8583478070306984\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8239840989399293\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3731.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5786253350893751\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 139.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5418034619877795\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.2881615420259378\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6444519053214705\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.525\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 252.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7699137038311841\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 76.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8613533396018784\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.6317339982624571\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7237915129794692\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8223140495867769\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 199.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.7197024434968179\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 69.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7673286760135991\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.5816523666500073\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.7156027594069841\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.7238493723849372\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 173.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02672216109931469, 0.050564832985401154)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.005339046008884907, 0.04642191901803017)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.02404787391424179, 0.036993011832237244)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8286459576636108\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8598834448789769\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.6020687114462753\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8451358293392155\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.8127872553404468\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 229.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7792807200270747\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 66.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.8210371031255902\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5950284807107048\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.782615716624074\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.775974025974026\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 239.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.02971027046442032, 0.04706913232803345)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.005279891658574343, 0.04475434869527817)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.017423996701836586, 0.02237606607377529)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002579535683577)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : der_replay\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/3c808429d10a497c85a0d9ed321dfed7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/der_replay\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (2.98 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_DER.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--DER_enable \\\n",
    "--DER_old_model \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "--name der_replay \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 这是没有数据回放的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e6549070-1200-4f79-b088-2c31f9470673",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/der_replay/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 105766d2 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 384 layers, 14269526 parameters, 0 gradients, 81.1 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.869       0.79      0.863      0.579\n",
      "                   car       2244       8711      0.918      0.892      0.953      0.732\n",
      "                   van       2244        861      0.887      0.857      0.912      0.684\n",
      "                 truck       2244        333      0.912       0.94      0.967      0.763\n",
      "                  tram       2244        138      0.897      0.899      0.954      0.631\n",
      "                person       2244       1286      0.881      0.683       0.78      0.421\n",
      "        person_sitting       2244         89      0.707      0.515       0.62      0.307\n",
      "               cyclist       2244        496       0.85      0.743      0.843      0.503\n",
      "                  misc       2244        284        0.9      0.791      0.876      0.589\n",
      "Speed: 0.0ms pre-process, 1.6ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp273\u001b[0m\n",
      "Vis\n"
     ]
    }
   ],
   "source": [
    "# 显存占用过于夸张。\n",
    "model = f'runs/train/der_replay/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Vis' \\\n",
    "\" \n",
    "!{val_command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f547002b-d3c6-4e02-9600-fe5a7a3912e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adeb3956-0c47-4671-b925-499cd6fe84d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d145ed4-9698-49ed-8a8e-7865a3333862",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f177398a-b9ba-44da-9b55-3af8330ee438",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f2f3735-442a-4d35-8578-913e2ff25f49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39bb2d61-3aac-4a26-b1f0-8300bb26cb03",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ae3f632-8bce-4bbd-b22f-be0e3264efaf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66e5e300-2c1c-4aa9-ae5c-791f8eb763e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29f99e62-51a8-4abe-aa56-d1697804685e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_DER: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=Lwf_with_head, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[0.001], Lwf_temperature=1.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=True, DER_old_model=[]\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/57a98d49e49c46278e2cab905e76c5d5\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "首次创建 extractors\n",
      "成功拼接 extractors\n",
      "extractors共有模型个数： 1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_DerTest.Detect              [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "已知类别： 0\n",
      "YOLOv5s_VOCKITTI summary: 226 layers, 7176067 parameters, 7176067 gradients, 64.9 GFLOPs\n",
      "\n",
      "Transferred 342/723 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_DerTest.Detect              [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 75 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/Lwf_with_head/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/Lwf_with_head\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       3.5G    0.04959    0.01876    0.03678         98        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/49       3.5G    0.07126     0.0451    0.07103         36        640: 1\n",
      "tensor([4.70412], device='cuda:0', grad_fn=<AddBackward0>) tensor(2265.02051, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.668     0.0283     0.0194    0.00671\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.09G    0.06097    0.04219    0.06606         58        640: 1\n",
      "tensor([5.39474], device='cuda:0', grad_fn=<AddBackward0>) tensor(2679.85034, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.443     0.0677     0.0604     0.0247\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.09G    0.05423    0.04087    0.05953         37        640: 1\n",
      "tensor([6.90310], device='cuda:0', grad_fn=<AddBackward0>) tensor(4966.13281, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.429      0.134      0.101     0.0425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.09G    0.05078    0.03942    0.05446         46        640: 1\n",
      "tensor([6.97117], device='cuda:0', grad_fn=<AddBackward0>) tensor(4990.92334, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.329      0.215       0.16     0.0726\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.09G    0.05036    0.03967    0.05123         39        640: 1\n",
      "tensor([6.56034], device='cuda:0', grad_fn=<AddBackward0>) tensor(4570.84473, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.332      0.271      0.215      0.099\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.09G    0.04865    0.03886    0.04667         28        640: 1\n",
      "tensor([6.58787], device='cuda:0', grad_fn=<AddBackward0>) tensor(4909.17920, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.31      0.319      0.254      0.116\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.09G    0.04806    0.03845     0.0437         39        640: 1\n",
      "tensor([6.53050], device='cuda:0', grad_fn=<AddBackward0>) tensor(4844.03223, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.339      0.342      0.284      0.128\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.09G    0.04804    0.03833    0.04314         34        640: 1\n",
      "tensor([7.10091], device='cuda:0', grad_fn=<AddBackward0>) tensor(5438.51562, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.384      0.346      0.312      0.139\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.09G    0.04689    0.03822     0.0413         41        640: 1\n",
      "tensor([5.47187], device='cuda:0', grad_fn=<AddBackward0>) tensor(3982.78076, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.408      0.374      0.341      0.162\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.09G    0.04682     0.0374    0.04035         46        640: 1\n",
      "tensor([6.13038], device='cuda:0', grad_fn=<AddBackward0>) tensor(4380.22754, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.406      0.382      0.347      0.163\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.09G    0.04668    0.03837    0.03972         30        640: 1\n",
      "tensor([5.94243], device='cuda:0', grad_fn=<AddBackward0>) tensor(4255.28613, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.42      0.397      0.365      0.177\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.09G    0.04543    0.03794    0.03963         26        640: 1\n",
      "tensor([5.86254], device='cuda:0', grad_fn=<AddBackward0>) tensor(4325.40674, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.409      0.397      0.367      0.176\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.09G    0.04527    0.03804     0.0388         33        640: 1\n",
      "tensor([5.47614], device='cuda:0', grad_fn=<AddBackward0>) tensor(3961.66895, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.436      0.392      0.375      0.183\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.09G    0.04556    0.03719    0.03794         30        640: 1\n",
      "tensor([5.35688], device='cuda:0', grad_fn=<AddBackward0>) tensor(3789.60913, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.455      0.409      0.403      0.199\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.09G    0.04566    0.03759    0.03693         33        640: 1\n",
      "tensor([5.75670], device='cuda:0', grad_fn=<AddBackward0>) tensor(4095.31519, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.448      0.416      0.403      0.199\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.09G    0.04582      0.037    0.03693         23        640: 1\n",
      "tensor([4.70991], device='cuda:0', grad_fn=<AddBackward0>) tensor(3122.90088, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.451      0.425      0.403      0.201\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.09G    0.04529    0.03844    0.03677         40        640: 1\n",
      "tensor([5.41845], device='cuda:0', grad_fn=<AddBackward0>) tensor(3709.16187, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.448      0.423        0.4        0.2\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.09G    0.04451    0.03823    0.03606         45        640: 1\n",
      "tensor([4.67231], device='cuda:0', grad_fn=<AddBackward0>) tensor(3185.97632, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.466      0.432      0.414      0.207\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.09G    0.04418    0.03761    0.03549         20        640: 1\n",
      "tensor([4.68379], device='cuda:0', grad_fn=<AddBackward0>) tensor(3580.73096, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.459      0.436      0.422      0.213\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.09G    0.04453    0.03811    0.03636         38        640: 1\n",
      "tensor([4.69339], device='cuda:0', grad_fn=<AddBackward0>) tensor(2942.00488, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.468      0.438      0.424      0.215\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.09G    0.04441     0.0372    0.03502         33        640: 1\n",
      "tensor([4.64944], device='cuda:0', grad_fn=<AddBackward0>) tensor(3021.22949, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.472      0.439      0.424      0.215\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.09G    0.04492    0.03763    0.03596         28        640: 1\n",
      "tensor([4.28772], device='cuda:0', grad_fn=<AddBackward0>) tensor(2989.58350, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.466      0.433      0.426      0.217\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.09G    0.04433    0.03771    0.03471         27        640: 1\n",
      "tensor([4.78209], device='cuda:0', grad_fn=<AddBackward0>) tensor(3323.50537, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.481      0.445      0.438      0.223\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.09G    0.04392    0.03691    0.03503         29        640: 1\n",
      "tensor([5.47278], device='cuda:0', grad_fn=<AddBackward0>) tensor(3755.41211, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.481       0.46      0.444      0.227\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.09G    0.04388    0.03664    0.03553         35        640: 1\n",
      "tensor([4.07074], device='cuda:0', grad_fn=<AddBackward0>) tensor(2716.91992, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.485      0.457      0.445      0.229\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.09G    0.04394    0.03778    0.03466         31        640: 1\n",
      "tensor([4.29092], device='cuda:0', grad_fn=<AddBackward0>) tensor(2935.28491, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.488      0.455      0.445      0.228\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.09G    0.04418    0.03658    0.03492         40        640: 1\n",
      "tensor([4.19374], device='cuda:0', grad_fn=<AddBackward0>) tensor(2398.10669, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.496      0.453       0.45       0.23\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.09G    0.04412    0.03787     0.0348         29        640: 1\n",
      "tensor([3.66057], device='cuda:0', grad_fn=<AddBackward0>) tensor(2267.82251, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.479      0.458      0.446      0.228\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.09G    0.04353    0.03684    0.03336         26        640: 1\n",
      "tensor([3.57468], device='cuda:0', grad_fn=<AddBackward0>) tensor(2284.78076, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.491      0.457      0.454      0.233\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.09G    0.04337    0.03731    0.03417         45        640: 1\n",
      "tensor([4.01264], device='cuda:0', grad_fn=<AddBackward0>) tensor(2332.51245, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.504      0.452      0.457      0.234\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.09G    0.04339    0.03718    0.03373         36        640: 1\n",
      "tensor([3.74201], device='cuda:0', grad_fn=<AddBackward0>) tensor(2303.62842, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.493      0.461      0.458      0.236\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.09G    0.04371    0.03634    0.03389         20        640: 1\n",
      "tensor([4.07418], device='cuda:0', grad_fn=<AddBackward0>) tensor(2399.32666, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.493      0.462      0.459      0.238\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.09G    0.04339    0.03609    0.03444         25        640: 1\n",
      "tensor([3.26403], device='cuda:0', grad_fn=<AddBackward0>) tensor(1871.54504, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.495      0.466      0.459      0.236\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.09G    0.04405     0.0372    0.03392         34        640: 1\n",
      "tensor([3.82484], device='cuda:0', grad_fn=<AddBackward0>) tensor(2129.25366, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.495      0.467      0.463      0.239\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.09G    0.04332    0.03809    0.03396         47        640: 1\n",
      "tensor([3.22451], device='cuda:0', grad_fn=<AddBackward0>) tensor(1715.43958, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.493      0.471      0.462      0.239\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.09G    0.04343    0.03756    0.03356         35        640: 1\n",
      "tensor([3.55641], device='cuda:0', grad_fn=<AddBackward0>) tensor(1869.76562, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.497      0.473      0.464       0.24\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.09G    0.04313    0.03837    0.03377         41        640: 1\n",
      "tensor([3.55752], device='cuda:0', grad_fn=<AddBackward0>) tensor(1907.54907, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.485      0.476      0.461       0.24\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.09G    0.04363    0.03816    0.03415         41        640: 1\n",
      "tensor([3.87554], device='cuda:0', grad_fn=<AddBackward0>) tensor(1930.49121, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.5      0.468      0.464      0.243\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.09G    0.04354     0.0371    0.03436         23        640: 1\n",
      "tensor([3.32801], device='cuda:0', grad_fn=<AddBackward0>) tensor(1685.73193, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.496      0.468      0.462       0.24\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.09G    0.04323    0.03639    0.03508         29        640: 1\n",
      "tensor([3.18504], device='cuda:0', grad_fn=<AddBackward0>) tensor(1576.60535, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.489      0.471      0.463      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.09G    0.04318    0.03738    0.03358         33        640: 1\n",
      "tensor([3.02264], device='cuda:0', grad_fn=<AddBackward0>) tensor(1367.35364, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.489      0.478      0.467      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.09G    0.04245     0.0374    0.03414         33        640: 1\n",
      "tensor([3.15626], device='cuda:0', grad_fn=<AddBackward0>) tensor(1499.48157, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.505      0.461      0.465      0.241\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.09G     0.0426    0.03691    0.03409         34        640: 1\n",
      "tensor([2.78944], device='cuda:0', grad_fn=<AddBackward0>) tensor(1200.90381, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.499       0.47      0.463      0.239\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.09G    0.04284    0.03733    0.03298         35        640: 1\n",
      "tensor([2.96891], device='cuda:0', grad_fn=<AddBackward0>) tensor(1363.18774, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.5      0.471      0.467      0.241\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.09G    0.04241    0.03751    0.03287         33        640: 1\n",
      "tensor([2.73908], device='cuda:0', grad_fn=<AddBackward0>) tensor(1344.40271, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.495      0.471      0.466      0.241\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.09G    0.04272    0.03704    0.03339         34        640: 1\n",
      "tensor([3.07201], device='cuda:0', grad_fn=<AddBackward0>) tensor(1281.64575, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.492      0.478      0.467      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.09G    0.04213    0.03699     0.0334         40        640: 1\n",
      "tensor([2.91518], device='cuda:0', grad_fn=<AddBackward0>) tensor(964.34570, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.497      0.466      0.465      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.09G    0.04287    0.03804    0.03264         42        640: 1\n",
      "tensor([2.53909], device='cuda:0', grad_fn=<AddBackward0>) tensor(1059.33801, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.493      0.472      0.467      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.09G    0.04181    0.03719    0.03287         26        640: 1\n",
      "tensor([2.23871], device='cuda:0', grad_fn=<AddBackward0>) tensor(1206.52319, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.495      0.476      0.468      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.09G    0.04221    0.03653    0.03312         21        640: 1\n",
      "tensor([2.25242], device='cuda:0', grad_fn=<AddBackward0>) tensor(959.28107, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.502       0.47      0.469      0.243\n",
      "\n",
      "50 epochs completed in 2.403 hours.\n",
      "Optimizer stripped from runs/train/Lwf_with_head/weights/last.pt, 14.7MB\n",
      "Optimizer stripped from runs/train/Lwf_with_head/weights/best.pt, 14.7MB\n",
      "\n",
      "Validating runs/train/Lwf_with_head/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 169 layers, 7166563 parameters, 0 gradients, 64.2 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.501      0.472      0.468      0.242\n",
      "                   car       4952       1201      0.667      0.717      0.737       0.48\n",
      "                person       4952       4528      0.657      0.594      0.648      0.327\n",
      "             aeroplane       4952        285      0.503       0.53      0.526      0.248\n",
      "               bicycle       4952        337      0.656      0.515      0.595      0.314\n",
      "                  bird       4952        459       0.38      0.311      0.283      0.134\n",
      "                  boat       4952        263      0.314      0.433      0.318      0.136\n",
      "                bottle       4952        469      0.431      0.488      0.447       0.23\n",
      "                   bus       4952        213      0.556      0.568      0.596      0.404\n",
      "                   cat       4952        358      0.571      0.342      0.418       0.18\n",
      "                 chair       4952        756      0.424      0.377      0.359      0.187\n",
      "                   cow       4952        244      0.477      0.541      0.455      0.268\n",
      "           diningtable       4952        206      0.422      0.282       0.33      0.111\n",
      "                   dog       4952        489      0.514      0.256      0.354      0.158\n",
      "                 horse       4952        348      0.528      0.579      0.506      0.232\n",
      "             motorbike       4952        325      0.612      0.578      0.602      0.306\n",
      "           pottedplant       4952        480      0.374      0.321      0.276      0.105\n",
      "                 sheep       4952        242      0.422      0.607      0.556      0.348\n",
      "                  sofa       4952        239      0.469      0.347      0.356      0.189\n",
      "                 train       4952        282       0.57      0.535      0.531      0.239\n",
      "             tvmonitor       4952        308      0.466      0.516      0.472      0.252\n",
      "Results saved to \u001b[1mruns/train/Lwf_with_head\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : Lwf_with_head\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/57a98d49e49c46278e2cab905e76c5d5\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.5160875500445998\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 149.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.5264201872458306\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.2481057725675721\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.5030448684195454\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.5298245614035088\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 151.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.5770540332148382\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 91.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.5951776060395959\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.3137522495554488\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.6560432430715449\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.5150417479794334\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 174.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.34170444180566517\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 234.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.28316636400965073\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.13370354978659973\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.37964508835076205\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.310658152706083\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 143.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.36405690071241104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 249.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.31825992402116254\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.13620521994507076\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.31381122290213204\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.43346007604562736\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 114.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.45792112884179514\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 302.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.44743222851303116\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.22957568197169867\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.4311219296929807\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.488272921108742\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 229.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.5617759625370996\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 97.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.5957464283215236\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.4043963949367407\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.5556149731941387\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.568075117370892\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 121.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.6910781760027411\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 430.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.737227602865658\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.4803028384549606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.6670495852074941\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.7169025811823481\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 861.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.42774215434176155\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 92.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.418032978072989\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.18045103918927136\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.5709441238674013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.3419704620821939\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 122.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.3992406850915826\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 387.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.35922553491268716\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.18663738549211417\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.42429011537618094\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.376984126984127\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 285.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.506754244513169\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 145.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.45519524920005955\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.26787519754869427\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.4765986752723529\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.5409836065573771\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 132.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.33777306080798536\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 79.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.33042458777809985\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.11119156835105186\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.4220457842799956\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.2815533980582524\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 58.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.34150437591200283\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 118.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.3542470743203271\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.1577796601074866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.5142868024875715\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.2556237218813906\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 125.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.5522107476902981\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 180.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.5064924444439305\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.23246718071993172\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.5280390874446198\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.5787015463739601\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 201.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (3.33428692817688, 42.00952911376953)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.019445194833571034, 0.4686259335688967)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.006713816919796138, 0.24292882785942962)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.31043213404278064, 0.668190814105181)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.028272127835166805, 0.4783974876267013)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.5942924199746693\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 119.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.6020065146753353\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.30615712215301427\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.6120111425214574\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.5775708015708015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 188.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.6235944444503257\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 1404.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.6477829785051838\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.32672997300734447\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.6568539685846756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.5935407580725601\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 2688.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.3455287165951997\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 257.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.2763193413461779\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.10543244124036029\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.37434286919617515\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.32083333333333336\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 154.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.49801585466528314\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 201.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.5560690153904368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.3481226638407886\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.42199828094255526\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.6074380165289256\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 147.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.3991859501940703\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 94.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.35621614117967265\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.18868212869909104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.4693341080390869\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.3472803347280335\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 83.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.04180922731757164, 0.07126151770353317)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.03264003247022629, 0.07103317230939865)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.03609413653612137, 0.045104287564754486)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.5519604156261809\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 114.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.5314518764472143\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.23929740461624677\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.5695089755665724\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.5354609929078015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 151.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.4900975045802614\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 182.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.4723267036542547\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.25221386234170595\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.46648020281796376\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.5162337662337663\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 159.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.041103776544332504, 0.06664574891328812)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.02487807907164097, 0.06274454295635223)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.023104321211576462, 0.028128892183303833)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : Lwf_with_head\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/57a98d49e49c46278e2cab905e76c5d5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/Lwf_with_head\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.33 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_DER.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--DER_enable \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-3 \\\n",
    "--Old_models \\\n",
    "    ./runs/train/fog_02/weights/last.pt \\\n",
    "--name Lwf_with_head \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 这是没有数据回放的\n",
    "# 三个小时还是太离谱了一点\n",
    "# --DER_old_model \\\n",
    "#    ./runs/train/fog_02/weights/last.pt \\\n",
    "# base -0.197, 0.364"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c6aeb6a-dda9-497a-ba57-9b164d51a70c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fd123e1-1cbe-41d0-bf03-1ae2e4d1078e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2d8519e-3f51-4dae-b454-a5d0f0eaba7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3af162e0-0f9c-48a6-be2a-efeba5d7bab5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a07e9c4-f833-414f-84db-d755ae10e5c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c315b5d9-82a8-4c8c-808c-7681c9928003",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_DER: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=replay_Lwf_with_head, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[0.0001], Lwf_temperature=1.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=True, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2900 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/f7788f13f000407a9e3d3640d0f3d295\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "首次创建 extractors\n",
      "成功拼接 extractors\n",
      "extractors共有模型个数： 1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_DerTest.Detect              [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "已知类别： 0\n",
      "YOLOv5s_VOCKITTI summary: 226 layers, 7176067 parameters, 7176067 gradients, 64.9 GFLOPs\n",
      "\n",
      "Transferred 342/723 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_DerTest.Detect              [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 75 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old... 18599 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.17 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/replay_Lwf_with_head/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/replay_Lwf_with_head\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       3.5G    0.05164    0.03919    0.04774         64        640: 1\n",
      "tensor([2.80373], device='cuda:0', grad_fn=<AddBackward0>) tensor(4086.53247, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.961     0.0523     0.0871     0.0408\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.08G    0.04733    0.03525    0.03758         21        640: 1\n",
      "tensor([2.21883], device='cuda:0', grad_fn=<AddBackward0>) tensor(6425.39795, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.342      0.299      0.236      0.116\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.08G    0.04595      0.035    0.03018         31        640: 1\n",
      "tensor([2.45773], device='cuda:0', grad_fn=<AddBackward0>) tensor(9702.01172, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.435       0.44      0.399      0.199\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.08G    0.04384    0.03446    0.02539         29        640: 1\n",
      "tensor([2.08358], device='cuda:0', grad_fn=<AddBackward0>) tensor(9623.30078, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.537      0.528      0.519      0.263\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.08G    0.04297    0.03448    0.02273         44        640: 1\n",
      "tensor([2.16028], device='cuda:0', grad_fn=<AddBackward0>) tensor(9052.15820, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.607      0.563      0.587      0.319\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.08G    0.04211    0.03431    0.02158         30        640: 1\n",
      "tensor([2.14304], device='cuda:0', grad_fn=<AddBackward0>) tensor(10483.35938, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.632      0.578      0.615      0.334\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.08G    0.04133    0.03346    0.02016         44        640: 1\n",
      "tensor([2.13068], device='cuda:0', grad_fn=<AddBackward0>) tensor(9214.30859, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.628      0.598      0.629       0.36\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.08G    0.03996    0.03264    0.01901         41        640: 1\n",
      "tensor([2.26296], device='cuda:0', grad_fn=<AddBackward0>) tensor(10096.15723, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.644        0.6      0.645      0.367\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.08G     0.0391    0.03272    0.01841         35        640: 1\n",
      "tensor([1.96530], device='cuda:0', grad_fn=<AddBackward0>) tensor(9353.96094, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.67      0.622      0.661      0.388\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.08G      0.039    0.03281    0.01774         33        640: 1\n",
      "tensor([2.00845], device='cuda:0', grad_fn=<AddBackward0>) tensor(9222.06641, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.684       0.62      0.677      0.407\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.08G    0.03842    0.03229     0.0172         59        640: 1\n",
      "tensor([2.07394], device='cuda:0', grad_fn=<AddBackward0>) tensor(8553.84766, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.685      0.632      0.688      0.405\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.08G    0.03825    0.03243    0.01692         34        640: 1\n",
      "tensor([2.09475], device='cuda:0', grad_fn=<AddBackward0>) tensor(9194.56348, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.686      0.652      0.694      0.413\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.08G    0.03787     0.0318    0.01636         56        640: 1\n",
      "tensor([2.34233], device='cuda:0', grad_fn=<AddBackward0>) tensor(9389.19434, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.703      0.655      0.707      0.427\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.08G    0.03713      0.031    0.01567         36        640: 1\n",
      "tensor([2.10195], device='cuda:0', grad_fn=<AddBackward0>) tensor(8840.20020, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.69      0.649      0.694      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.08G    0.03719    0.03198    0.01519         69        640: 1\n",
      "tensor([2.46335], device='cuda:0', grad_fn=<AddBackward0>) tensor(8599.33105, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.716      0.666      0.717      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.08G    0.03671    0.03149    0.01473         51        640: 1\n",
      "tensor([2.12128], device='cuda:0', grad_fn=<AddBackward0>) tensor(8761.75781, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.713      0.667      0.722      0.454\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.08G    0.03639     0.0313     0.0146         43        640: 1\n",
      "tensor([2.16873], device='cuda:0', grad_fn=<AddBackward0>) tensor(9242.20312, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.674      0.723      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.08G    0.03644    0.03067    0.01405         27        640: 1\n",
      "tensor([2.16124], device='cuda:0', grad_fn=<AddBackward0>) tensor(10534.34277, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734      0.677      0.732      0.461\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.08G    0.03557    0.03036    0.01379         33        640: 1\n",
      "tensor([2.14427], device='cuda:0', grad_fn=<AddBackward0>) tensor(8898.70508, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.727      0.684       0.74      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.08G    0.03633    0.03082    0.01344         46        640: 1\n",
      "tensor([2.05172], device='cuda:0', grad_fn=<AddBackward0>) tensor(7778.16748, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.72        0.7      0.743      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.08G    0.03525    0.03075    0.01319         46        640: 1\n",
      "tensor([2.25268], device='cuda:0', grad_fn=<AddBackward0>) tensor(10488.59277, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.717      0.699      0.745      0.477\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.08G    0.03603    0.03011    0.01297         29        640: 1\n",
      "tensor([2.19962], device='cuda:0', grad_fn=<AddBackward0>) tensor(9690.41504, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.727      0.688      0.746       0.48\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.08G    0.03461    0.02992    0.01357         35        640: 1\n",
      "tensor([2.06142], device='cuda:0', grad_fn=<AddBackward0>) tensor(9580.25098, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.729      0.701      0.749      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.08G    0.03457    0.02985     0.0129         37        640: 1\n",
      "tensor([1.75964], device='cuda:0', grad_fn=<AddBackward0>) tensor(7945.66895, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.726      0.712      0.752      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.08G    0.03406    0.02942    0.01256         31        640: 1\n",
      "tensor([1.90485], device='cuda:0', grad_fn=<AddBackward0>) tensor(9781.78516, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.701      0.759      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.08G    0.03424     0.0295    0.01228         47        640: 1\n",
      "tensor([2.44237], device='cuda:0', grad_fn=<AddBackward0>) tensor(10117.11035, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.735      0.716      0.763      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.08G    0.03398     0.0296     0.0124         40        640: 1\n",
      "tensor([1.77896], device='cuda:0', grad_fn=<AddBackward0>) tensor(8693.16992, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.74      0.716      0.766      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.08G    0.03378    0.02944    0.01171         31        640: 1\n",
      "tensor([1.95283], device='cuda:0', grad_fn=<AddBackward0>) tensor(9789.87500, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747      0.713      0.765      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.08G    0.03349    0.02905    0.01118         26        640: 1\n",
      "tensor([1.64488], device='cuda:0', grad_fn=<AddBackward0>) tensor(8590.95117, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.718       0.77      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.08G    0.03296    0.02829    0.01178         33        640: 1\n",
      "tensor([2.12810], device='cuda:0', grad_fn=<AddBackward0>) tensor(9410.06348, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747      0.721      0.771      0.509\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.08G    0.03264    0.02906    0.01132         40        640: 1\n",
      "tensor([1.95678], device='cuda:0', grad_fn=<AddBackward0>) tensor(8691.74316, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751      0.714       0.77       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.08G    0.03201    0.02846    0.01053         40        640: 1\n",
      "tensor([1.88829], device='cuda:0', grad_fn=<AddBackward0>) tensor(9883.59375, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751       0.72      0.773      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.08G    0.03234    0.02875    0.01075         33        640: 1\n",
      "tensor([1.54478], device='cuda:0', grad_fn=<AddBackward0>) tensor(7431.04053, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.742       0.73      0.775      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.08G    0.03243    0.02866    0.01059         36        640: 1\n",
      "tensor([1.96381], device='cuda:0', grad_fn=<AddBackward0>) tensor(8450.51855, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.748      0.728      0.775      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.08G    0.03181    0.02848    0.01002         44        640: 1\n",
      "tensor([2.05251], device='cuda:0', grad_fn=<AddBackward0>) tensor(9456.23438, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.721      0.778       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.08G    0.03122    0.02816   0.009889         34        640: 1\n",
      "tensor([1.69541], device='cuda:0', grad_fn=<AddBackward0>) tensor(8885.34668, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.733      0.778      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.08G    0.03181    0.02792   0.009992         56        640: 1\n",
      "tensor([1.88334], device='cuda:0', grad_fn=<AddBackward0>) tensor(7758.15576, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.734      0.781      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.08G    0.03123    0.02796   0.009409         44        640: 1\n",
      "tensor([1.63526], device='cuda:0', grad_fn=<AddBackward0>) tensor(7165.66504, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.754       0.74      0.782      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.08G    0.03081    0.02703   0.009355         34        640: 1\n",
      "tensor([1.61939], device='cuda:0', grad_fn=<AddBackward0>) tensor(7616.16064, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.727      0.782      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.08G     0.0305    0.02742   0.009611         48        640: 1\n",
      "tensor([1.95077], device='cuda:0', grad_fn=<AddBackward0>) tensor(8354.33789, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.727      0.783       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.08G    0.03043    0.02734   0.009138         56        640: 1\n",
      "tensor([2.09628], device='cuda:0', grad_fn=<AddBackward0>) tensor(9611.76074, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.733      0.782      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.08G    0.03026    0.02732   0.008867         36        640: 1\n",
      "tensor([1.65141], device='cuda:0', grad_fn=<AddBackward0>) tensor(7927.86426, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.737      0.784      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.08G    0.03032    0.02757   0.009422         41        640: 1\n",
      "tensor([2.01512], device='cuda:0', grad_fn=<AddBackward0>) tensor(8156.20312, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.734      0.783      0.533\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.08G    0.02939      0.027   0.008524         38        640: 1\n",
      "tensor([1.56333], device='cuda:0', grad_fn=<AddBackward0>) tensor(7865.38232, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.734      0.784      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.08G    0.02943    0.02583   0.008755         21        640: 1\n",
      "tensor([1.43258], device='cuda:0', grad_fn=<AddBackward0>) tensor(8261.78516, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.735      0.782      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.08G    0.02914    0.02641   0.008446         40        640: 1\n",
      "tensor([1.79994], device='cuda:0', grad_fn=<AddBackward0>) tensor(9298.31250, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755       0.74      0.781       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.08G    0.02904    0.02644   0.008514         40        640: 1\n",
      "tensor([1.94846], device='cuda:0', grad_fn=<AddBackward0>) tensor(7899.66406, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.761      0.732       0.78       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.08G     0.0286    0.02586   0.008189         31        640: 1\n",
      "tensor([1.52005], device='cuda:0', grad_fn=<AddBackward0>) tensor(7567.87354, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.731       0.78       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.08G    0.02806    0.02564   0.007911         26        640: 1\n",
      "tensor([1.46998], device='cuda:0', grad_fn=<AddBackward0>) tensor(7750.99023, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.761      0.734       0.78       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.08G    0.02849    0.02635   0.007924         30        640: 1\n",
      "tensor([1.48134], device='cuda:0', grad_fn=<AddBackward0>) tensor(6953.02539, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.736       0.78       0.53\n",
      "\n",
      "50 epochs completed in 2.650 hours.\n",
      "Optimizer stripped from runs/train/replay_Lwf_with_head/weights/last.pt, 14.7MB\n",
      "Optimizer stripped from runs/train/replay_Lwf_with_head/weights/best.pt, 14.7MB\n",
      "\n",
      "Validating runs/train/replay_Lwf_with_head/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 169 layers, 7166563 parameters, 0 gradients, 64.2 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.758      0.733      0.783      0.533\n",
      "                   car       4952       1201      0.815      0.883      0.909      0.686\n",
      "                person       4952       4528      0.833      0.799      0.869      0.565\n",
      "             aeroplane       4952        285      0.903      0.786       0.87      0.565\n",
      "               bicycle       4952        337      0.861       0.78      0.861      0.595\n",
      "                  bird       4952        459      0.732       0.66      0.728       0.46\n",
      "                  boat       4952        263      0.574       0.62      0.626      0.366\n",
      "                bottle       4952        469       0.67      0.689      0.714      0.477\n",
      "                   bus       4952        213      0.822      0.821      0.877       0.72\n",
      "                   cat       4952        358      0.849      0.753      0.815      0.561\n",
      "                 chair       4952        756      0.607      0.581      0.628        0.4\n",
      "                   cow       4952        244      0.742      0.799      0.819      0.599\n",
      "           diningtable       4952        206      0.716       0.65      0.732      0.482\n",
      "                   dog       4952        489      0.799      0.706       0.81      0.541\n",
      "                 horse       4952        348      0.858       0.83      0.886      0.626\n",
      "             motorbike       4952        325      0.818        0.8      0.865      0.551\n",
      "           pottedplant       4952        480      0.605      0.506      0.518      0.264\n",
      "                 sheep       4952        242      0.701      0.806      0.825      0.592\n",
      "                  sofa       4952        239      0.671      0.665      0.711      0.508\n",
      "                 train       4952        282      0.829      0.784      0.829      0.551\n",
      "             tvmonitor       4952        308      0.747      0.747      0.776      0.546\n",
      "Results saved to \u001b[1mruns/train/replay_Lwf_with_head\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m The process of logging environment details (conda environment, git patch) is underway. Please be patient as this may take some time.\n",
      "^C\n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Experiment was interrupted by user while waiting for the initial data logger to be flushed.\n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Failed to complete logging of all environment details (conda environment, git patch)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : replay_Lwf_with_head\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/f7788f13f000407a9e3d3640d0f3d295\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8402636864721477\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 24.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8697174743057888\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.5649924288667888\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9026217563592871\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.7859649122807018\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 224.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8188200618906294\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.8614411085287639\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.5947444747729301\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8612001497550982\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7804154302670623\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 263.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.6940039318076053\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 111.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7280951881919947\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.4595448251632287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.731541434630603\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.6601307189542484\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 303.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.5959899600360508\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 121.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6255724811788818\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.36600718321014336\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.5739657311188402\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6197718631178707\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 163.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6790487082851617\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 159.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7140747033831231\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.47718702023380655\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6696647851858628\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6886993603411514\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 323.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8212884647818081\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 38.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8772398535042422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7201302894836105\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8215026961883783\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8210743450806048\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 175.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8473859644413287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 241.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9092582945422235\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.6860426526421525\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8147775915067549\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8827132016835042\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1060.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.7983220557871921\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 48.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8153596339942621\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.5606485294023911\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8489241776347702\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.7534130965229662\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 270.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.5936000818985702\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 284.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6283322237747988\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.400149500143612\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6070996293487244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5806878306878307\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 439.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7697951406698815\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 68.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8185884366595931\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.5985458751805426\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7424942526868823\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.7991803278688525\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 195.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6816148215555837\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7316680424977848\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.4818274995142203\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7158733871986549\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6504854368932039\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 134.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7492249572882718\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 87.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8097711164523459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.5406261815904922\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.7987004055578432\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7055214723926381\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 345.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.844170287541525\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 48.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.8863530721411329\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6256586836820418\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8583411134103518\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8304597701149425\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 289.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5815]                 : (1.7789621353149414, 11.123113632202148)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.08709234641327925, 0.7838583106505871)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.04079273736514051, 0.5326290986234837)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.3420108774437252, 0.9606185580984938)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.05227271222744297, 0.7403958628507671)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8086638558306732\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 58.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8651478450953308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5513002905918917\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8175174221867857\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 260.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8160054763344933\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 724.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8694122041500676\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.565262120832992\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8333203876990813\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.7993954643689626\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3620.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5513798086018168\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 158.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5178554165440757\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.26415205338427045\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6053433211377902\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.50625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 243.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.749845155935584\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 83.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8254657066769104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.5915337593480331\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7011680146511939\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8057851239669421\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 195.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6681104477055834\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 78.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7109086689563158\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.50754783288363\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.67097325427083\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6652719665271967\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 159.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02806379646062851, 0.051637884229421616)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.007910658605396748, 0.04774098098278046)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.025641921907663345, 0.03918809816241264)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8056485255066831\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 46.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8287267732167276\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.551329945176962\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8288753539098503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.7836879432624113\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 221.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7469718515255241\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 78.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7760480193832329\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5464797586392175\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7471905843241348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7467532467532467\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 230.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.031280238181352615, 0.04904354736208916)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.008927281014621258, 0.04985302314162254)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.01834831014275551, 0.024599246680736542)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002579535683577)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : replay_Lwf_with_head\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/f7788f13f000407a9e3d3640d0f3d295\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/replay_Lwf_with_head\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.06 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_DER.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--DER_enable \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-4 \\\n",
    "--Old_models \\\n",
    "    ./runs/train/fog_02/weights/last.pt \\\n",
    "--name replay_Lwf_with_head \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 这是没有数据回放的\n",
    "# 三个小时还是太离谱了一点\n",
    "# --DER_old_model \\\n",
    "#    ./runs/train/fog_02/weights/last.pt \\\n",
    "# 0.484, 0.59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32654200-210d-4c45-af50-e3cd6a6c2506",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTIBiC_base.yaml, weights=['runs/train/replay_Lwf_with_head/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 169 layers, 7166563 parameters, 0 gradients, 64.2 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.736       0.78       0.53\n",
      "                   car       4952       1201      0.819      0.878      0.905      0.686\n",
      "                person       4952       4528      0.838      0.797      0.868      0.568\n",
      "             aeroplane       4952        285      0.913      0.796      0.865      0.562\n",
      "               bicycle       4952        337      0.857      0.783      0.863      0.587\n",
      "                  bird       4952        459      0.725      0.653       0.72      0.459\n",
      "                  boat       4952        263      0.613      0.657      0.644      0.368\n",
      "                bottle       4952        469       0.66      0.676      0.704      0.469\n",
      "                   bus       4952        213      0.806      0.818      0.875      0.709\n",
      "                   cat       4952        358      0.844      0.757      0.805      0.549\n",
      "                 chair       4952        756      0.625      0.579      0.626      0.396\n",
      "                   cow       4952        244      0.722      0.803      0.827      0.602\n",
      "           diningtable       4952        206      0.713       0.65      0.721      0.484\n",
      "                   dog       4952        489      0.785      0.703      0.801       0.54\n",
      "                 horse       4952        348      0.874      0.839      0.889       0.62\n",
      "             motorbike       4952        325      0.831      0.822      0.857      0.549\n",
      "           pottedplant       4952        480      0.601      0.515      0.497      0.256\n",
      "                 sheep       4952        242      0.695      0.806      0.826      0.591\n",
      "                  sofa       4952        239      0.674       0.64      0.704       0.51\n",
      "                 train       4952        282      0.835       0.79      0.827      0.554\n",
      "             tvmonitor       4952        308      0.767      0.753      0.783      0.548\n",
      "Speed: 0.1ms pre-process, 1.5ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp353\u001b[0m\n",
      "Voc\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTIBiC_base.yaml, weights=['runs/train/replay_Lwf_with_head/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 169 layers, 7166563 parameters, 0 gradients, 64.2 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.736       0.78       0.53\n",
      "                   car       4952       1201      0.819      0.878      0.905      0.686\n",
      "                person       4952       4528      0.838      0.797      0.868      0.568\n",
      "             aeroplane       4952        285      0.913      0.796      0.865      0.562\n",
      "               bicycle       4952        337      0.857      0.783      0.863      0.587\n",
      "                  bird       4952        459      0.725      0.653       0.72      0.459\n",
      "                  boat       4952        263      0.613      0.657      0.644      0.368\n",
      "                bottle       4952        469       0.66      0.676      0.704      0.469\n",
      "                   bus       4952        213      0.806      0.818      0.875      0.709\n",
      "                   cat       4952        358      0.844      0.757      0.805      0.549\n",
      "                 chair       4952        756      0.625      0.579      0.626      0.396\n",
      "                   cow       4952        244      0.722      0.803      0.827      0.602\n",
      "           diningtable       4952        206      0.713       0.65      0.721      0.484\n",
      "                   dog       4952        489      0.785      0.703      0.801       0.54\n",
      "                 horse       4952        348      0.874      0.839      0.889       0.62\n",
      "             motorbike       4952        325      0.831      0.822      0.857      0.549\n",
      "           pottedplant       4952        480      0.601      0.515      0.497      0.256\n",
      "                 sheep       4952        242      0.695      0.806      0.826      0.591\n",
      "                  sofa       4952        239      0.674       0.64      0.704       0.51\n",
      "                 train       4952        282      0.835       0.79      0.827      0.554\n",
      "             tvmonitor       4952        308      0.767      0.753      0.783      0.548\n",
      "Speed: 0.1ms pre-process, 1.5ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp354\u001b[0m\n",
      "kitti\n"
     ]
    }
   ],
   "source": [
    "# 1e-4 1e-3\n",
    "model = f'runs/train/replay_Lwf_with_head/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Voc' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# kitti\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'kitti' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c375fe90-a672-49aa-b210-01229f126e0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68769a8e-8d41-49ed-8d03-35ea6e123b95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "098b3dd4-f524-4a57-b3d8-a4f52981c82b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_DER: \u001b[0mweights=./runs/train/increment_VOC_plain/weights/last.pt, cfg=models/yolov5s_openimages.yaml, data=data/openimages.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=80, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=Lwf_with_head_openimages, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[0.0001, 0.001], Lwf_temperature=1.0, Old_models=['./runs/train/increment_VOC_plain/weights/last.pt', './runs/train/fog_02/weights/last.pt'], DER_enable=True, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2900 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/efd83894537a4d629a08bdc7386a58f4\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "首次创建 extractors\n",
      "成功拼接 extractors\n",
      "extractors共有模型个数： 1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1    110583  models.yolo_DerTest.Detect              [36, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "已知类别： 0\n",
      "YOLOv5s_openimages summary: 226 layers, 7230007 parameters, 7230007 gradients, 65.3 GFLOPs\n",
      "\n",
      "Transferred 342/723 items from runs/train/increment_VOC_plain/weights/last.pt\n",
      "Overriding model.yaml nc=36 with nc=26\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_DerTest.Detect              [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_openimages summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Overriding model.yaml nc=36 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_DerTest.Detect              [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_openimages summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 75 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/openimages/labels/train.cache... 4200 \u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/openimages/labels/val.cache... 1200 imag\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.02 anchors/target, 0.998 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/Lwf_with_head_openimages2/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/Lwf_with_head_openimages2\u001b[0m\n",
      "Starting training for 80 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/79      3.72G    0.07327    0.03986    0.05314         40        640: 1\n",
      "tensor([20.16809], device='cuda:0', grad_fn=<AddBackward0>) tensor(26694.55273, device='cuda:0', grad_fn=<AddBackward0>), tensor(15242.20215, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.851     0.0467     0.0529     0.0241\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/79      5.82G     0.0604    0.03609    0.04211         63        640: 1\n",
      "tensor([18.61693], device='cuda:0', grad_fn=<AddBackward0>) tensor(24112.88867, device='cuda:0', grad_fn=<AddBackward0>), tensor(13572.90527, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.776      0.123      0.122     0.0644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/79      5.82G    0.05734    0.03573    0.03946         57        640: 1\n",
      "tensor([18.04365], device='cuda:0', grad_fn=<AddBackward0>) tensor(22592.73828, device='cuda:0', grad_fn=<AddBackward0>), tensor(13813.28320, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.688      0.156      0.164     0.0867\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/79      5.82G    0.05595    0.03551      0.037         42        640: 1\n",
      "tensor([16.26368], device='cuda:0', grad_fn=<AddBackward0>) tensor(21876.69336, device='cuda:0', grad_fn=<AddBackward0>), tensor(12197.13965, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.608      0.185      0.184     0.0992\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/79      5.82G    0.05433      0.035     0.0362         36        640: 1\n",
      "tensor([14.78020], device='cuda:0', grad_fn=<AddBackward0>) tensor(21490.53906, device='cuda:0', grad_fn=<AddBackward0>), tensor(10559.41309, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.615      0.214      0.205      0.108\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/79      5.82G     0.0536    0.03626    0.03444         39        640: 1\n",
      "tensor([15.41425], device='cuda:0', grad_fn=<AddBackward0>) tensor(22577.39453, device='cuda:0', grad_fn=<AddBackward0>), tensor(11295.71582, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.534      0.206      0.199      0.106\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/79      5.82G    0.05339    0.03581    0.03437         68        640: 1\n",
      "tensor([14.84174], device='cuda:0', grad_fn=<AddBackward0>) tensor(23135.47070, device='cuda:0', grad_fn=<AddBackward0>), tensor(9819.53809, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.587      0.239      0.226      0.124\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/79      5.82G     0.0524    0.03479    0.03402         31        640: 1\n",
      "tensor([13.10284], device='cuda:0', grad_fn=<AddBackward0>) tensor(23896.58789, device='cuda:0', grad_fn=<AddBackward0>), tensor(9131.76953, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.531      0.249      0.227      0.118\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/79      5.82G    0.05199    0.03537    0.03348         35        640: 1\n",
      "tensor([14.92015], device='cuda:0', grad_fn=<AddBackward0>) tensor(24494.28125, device='cuda:0', grad_fn=<AddBackward0>), tensor(10654.64746, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.517      0.241      0.219       0.12\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/79      5.82G    0.05142    0.03549    0.03314         42        640: 1\n",
      "tensor([14.14282], device='cuda:0', grad_fn=<AddBackward0>) tensor(23064.37305, device='cuda:0', grad_fn=<AddBackward0>), tensor(9797.50781, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.503      0.244      0.237      0.123\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/79      5.82G    0.05153    0.03443    0.03239         38        640: 1\n",
      "tensor([14.28890], device='cuda:0', grad_fn=<AddBackward0>) tensor(23486.98047, device='cuda:0', grad_fn=<AddBackward0>), tensor(10199.43262, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.531      0.239      0.231       0.12\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/79      5.82G    0.05033    0.03565    0.03176         59        640: 1\n",
      "tensor([14.80842], device='cuda:0', grad_fn=<AddBackward0>) tensor(23633.42188, device='cuda:0', grad_fn=<AddBackward0>), tensor(10271.93945, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.51      0.285      0.243      0.127\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/79      5.82G    0.05038    0.03498    0.03259         46        640: 1\n",
      "tensor([14.46511], device='cuda:0', grad_fn=<AddBackward0>) tensor(23735.92773, device='cuda:0', grad_fn=<AddBackward0>), tensor(10172.81738, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.451       0.28      0.227      0.123\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/79      5.82G    0.05041    0.03497    0.03125         47        640: 1\n",
      "tensor([14.39891], device='cuda:0', grad_fn=<AddBackward0>) tensor(23793.11328, device='cuda:0', grad_fn=<AddBackward0>), tensor(10088.26270, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.487      0.271      0.243      0.133\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/79      5.82G    0.04976    0.03356    0.03091         32        640: 1\n",
      "tensor([12.87895], device='cuda:0', grad_fn=<AddBackward0>) tensor(23917.23438, device='cuda:0', grad_fn=<AddBackward0>), tensor(9042.85352, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.486      0.306      0.238      0.133\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/79      5.82G    0.04897    0.03445    0.03024         48        640: 1\n",
      "tensor([13.16294], device='cuda:0', grad_fn=<AddBackward0>) tensor(22533.72266, device='cuda:0', grad_fn=<AddBackward0>), tensor(9038.77148, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.471      0.277      0.242      0.126\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/79      5.82G     0.0489    0.03391    0.03069         43        640: 1\n",
      "tensor([12.04914], device='cuda:0', grad_fn=<AddBackward0>) tensor(21153.38867, device='cuda:0', grad_fn=<AddBackward0>), tensor(8030.29688, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.456      0.277      0.232      0.128\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/79      5.82G    0.04904    0.03444    0.02981         64        640: 1\n",
      "tensor([13.35149], device='cuda:0', grad_fn=<AddBackward0>) tensor(22751.70312, device='cuda:0', grad_fn=<AddBackward0>), tensor(9254.73828, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.443      0.288      0.232       0.12\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/79      5.82G    0.04807    0.03467    0.02965         29        640: 1\n",
      "tensor([12.55363], device='cuda:0', grad_fn=<AddBackward0>) tensor(22651.55469, device='cuda:0', grad_fn=<AddBackward0>), tensor(8531.87695, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.46      0.262      0.245      0.129\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/79      5.82G    0.04803    0.03341    0.02938         39        640: 1\n",
      "tensor([12.33057], device='cuda:0', grad_fn=<AddBackward0>) tensor(22025.20703, device='cuda:0', grad_fn=<AddBackward0>), tensor(8234.84961, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.559      0.299      0.277      0.151\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/79      5.82G    0.04722    0.03384    0.02949         40        640: 1\n",
      "tensor([12.84082], device='cuda:0', grad_fn=<AddBackward0>) tensor(26107.43750, device='cuda:0', grad_fn=<AddBackward0>), tensor(8586.63965, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.425      0.281      0.249      0.139\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/79      5.82G    0.04766    0.03362     0.0294         44        640: 1\n",
      "tensor([12.46393], device='cuda:0', grad_fn=<AddBackward0>) tensor(21693.55469, device='cuda:0', grad_fn=<AddBackward0>), tensor(8452.78320, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.428      0.306      0.259      0.148\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/79      5.82G    0.04706    0.03298    0.02838         31        640: 1\n",
      "tensor([13.14127], device='cuda:0', grad_fn=<AddBackward0>) tensor(23174.34180, device='cuda:0', grad_fn=<AddBackward0>), tensor(9363.52832, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.543      0.299      0.258      0.143\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/79      5.82G    0.04646    0.03358    0.02811         72        640: 1\n",
      "tensor([13.14284], device='cuda:0', grad_fn=<AddBackward0>) tensor(24618.36133, device='cuda:0', grad_fn=<AddBackward0>), tensor(8835.18945, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.481      0.282      0.266      0.145\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/79      5.82G    0.04598    0.03337    0.02802         41        640: 1\n",
      "tensor([12.61458], device='cuda:0', grad_fn=<AddBackward0>) tensor(26986.22852, device='cuda:0', grad_fn=<AddBackward0>), tensor(8278.11328, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.463       0.28      0.275      0.152\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/79      5.82G    0.04625    0.03332    0.02815         33        640: 1\n",
      "tensor([12.76043], device='cuda:0', grad_fn=<AddBackward0>) tensor(23583.03125, device='cuda:0', grad_fn=<AddBackward0>), tensor(8643.63574, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.565       0.23      0.271      0.149\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/79      5.82G    0.04577    0.03264    0.02792         30        640: 1\n",
      "tensor([12.32346], device='cuda:0', grad_fn=<AddBackward0>) tensor(24067.86328, device='cuda:0', grad_fn=<AddBackward0>), tensor(8566.54199, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.527       0.27       0.27      0.146\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/79      5.82G    0.04586    0.03222    0.02786         30        640: 1\n",
      "tensor([11.82960], device='cuda:0', grad_fn=<AddBackward0>) tensor(23431.79297, device='cuda:0', grad_fn=<AddBackward0>), tensor(8099.06543, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.552      0.306      0.287      0.162\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/79      5.82G    0.04552    0.03288    0.02741         43        640: 1\n",
      "tensor([12.23971], device='cuda:0', grad_fn=<AddBackward0>) tensor(23810.32422, device='cuda:0', grad_fn=<AddBackward0>), tensor(8111.19336, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.483      0.286      0.288      0.165\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/79      5.82G     0.0454    0.03259    0.02725         62        640: 1\n",
      "tensor([12.27464], device='cuda:0', grad_fn=<AddBackward0>) tensor(23332.10156, device='cuda:0', grad_fn=<AddBackward0>), tensor(8016.15869, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.558      0.279      0.282      0.164\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/79      5.82G    0.04576    0.03273    0.02686         40        640: 1\n",
      "tensor([12.90878], device='cuda:0', grad_fn=<AddBackward0>) tensor(24225.69141, device='cuda:0', grad_fn=<AddBackward0>), tensor(8919.99512, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.512      0.282      0.264      0.144\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/79      5.82G     0.0448    0.03257    0.02647         37        640: 1\n",
      "tensor([12.64337], device='cuda:0', grad_fn=<AddBackward0>) tensor(24587.91602, device='cuda:0', grad_fn=<AddBackward0>), tensor(8681.46680, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.557      0.304      0.298      0.166\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/79      5.82G    0.04485    0.03273    0.02725         45        640: 1\n",
      "tensor([12.10631], device='cuda:0', grad_fn=<AddBackward0>) tensor(22091.09961, device='cuda:0', grad_fn=<AddBackward0>), tensor(8025.16797, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.499      0.309      0.268      0.149\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/79      5.82G    0.04469    0.03292    0.02702         70        640: 1\n",
      "tensor([12.41490], device='cuda:0', grad_fn=<AddBackward0>) tensor(22327.79297, device='cuda:0', grad_fn=<AddBackward0>), tensor(8388.16211, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.537      0.306      0.294      0.167\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/79      5.82G    0.04507     0.0317    0.02655         34        640: 1\n",
      "tensor([11.35213], device='cuda:0', grad_fn=<AddBackward0>) tensor(23372.62500, device='cuda:0', grad_fn=<AddBackward0>), tensor(7481.07373, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.552      0.303      0.293      0.161\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/79      5.82G    0.04391    0.03244    0.02622         52        640: 1\n",
      "tensor([11.67062], device='cuda:0', grad_fn=<AddBackward0>) tensor(23214.25586, device='cuda:0', grad_fn=<AddBackward0>), tensor(7691.19873, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.532      0.297      0.308      0.173\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/79      5.82G    0.04391    0.03217    0.02563         46        640: 1\n",
      "tensor([11.06292], device='cuda:0', grad_fn=<AddBackward0>) tensor(23350.78320, device='cuda:0', grad_fn=<AddBackward0>), tensor(7236.97900, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.504      0.294      0.293      0.168\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/79      5.82G    0.04406     0.0319    0.02589         42        640: 1\n",
      "tensor([11.75788], device='cuda:0', grad_fn=<AddBackward0>) tensor(23744.17773, device='cuda:0', grad_fn=<AddBackward0>), tensor(7675.58789, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.512       0.29      0.297      0.169\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/79      5.82G    0.04422     0.0319    0.02635         56        640: 1\n",
      "tensor([10.60119], device='cuda:0', grad_fn=<AddBackward0>) tensor(20844.69531, device='cuda:0', grad_fn=<AddBackward0>), tensor(6735.23730, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.51      0.304      0.288      0.165\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/79      5.82G    0.04422    0.03201    0.02594         47        640: 1\n",
      "tensor([12.70574], device='cuda:0', grad_fn=<AddBackward0>) tensor(26287.04883, device='cuda:0', grad_fn=<AddBackward0>), tensor(8151.66357, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.579      0.303      0.302      0.172\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/79      5.82G    0.04334    0.03149    0.02528         30        640: 1\n",
      "tensor([11.16429], device='cuda:0', grad_fn=<AddBackward0>) tensor(23952.66016, device='cuda:0', grad_fn=<AddBackward0>), tensor(7446.06494, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.501      0.305      0.298      0.178\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/79      5.82G    0.04306    0.03162    0.02489         31        640: 1\n",
      "tensor([10.84766], device='cuda:0', grad_fn=<AddBackward0>) tensor(23990.93164, device='cuda:0', grad_fn=<AddBackward0>), tensor(7286.86963, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.54      0.312      0.288      0.165\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/79      5.82G    0.04291    0.03082    0.02658         16        640: 1\n",
      "tensor([11.96781], device='cuda:0', grad_fn=<AddBackward0>) tensor(24949.33398, device='cuda:0', grad_fn=<AddBackward0>), tensor(8090.26270, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.58      0.295      0.295      0.183\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/79      5.82G    0.04287    0.03171    0.02506         39        640: 1\n",
      "tensor([11.75945], device='cuda:0', grad_fn=<AddBackward0>) tensor(22937.52539, device='cuda:0', grad_fn=<AddBackward0>), tensor(7925.97949, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.517      0.286      0.284      0.167\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/79      5.82G    0.04279     0.0315    0.02525         45        640: 1\n",
      "tensor([11.37369], device='cuda:0', grad_fn=<AddBackward0>) tensor(22450.97266, device='cuda:0', grad_fn=<AddBackward0>), tensor(7624.18115, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.528      0.283      0.278      0.159\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/79      5.82G    0.04302    0.03202    0.02583         50        640: 1\n",
      "tensor([11.57904], device='cuda:0', grad_fn=<AddBackward0>) tensor(23300.25195, device='cuda:0', grad_fn=<AddBackward0>), tensor(7526.87598, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.559      0.298      0.292      0.169\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/79      5.82G    0.04303    0.03108    0.02414         28        640: 1\n",
      "tensor([11.86354], device='cuda:0', grad_fn=<AddBackward0>) tensor(24621.36719, device='cuda:0', grad_fn=<AddBackward0>), tensor(7949.19727, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.531      0.301      0.293      0.175\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/79      5.82G    0.04133    0.03076    0.02538         36        640: 1\n",
      "tensor([11.03629], device='cuda:0', grad_fn=<AddBackward0>) tensor(22718.20312, device='cuda:0', grad_fn=<AddBackward0>), tensor(7348.26660, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.52      0.306      0.296      0.174\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/79      5.82G     0.0425    0.03105    0.02489         66        640: 1\n",
      "tensor([11.40543], device='cuda:0', grad_fn=<AddBackward0>) tensor(22286.53906, device='cuda:0', grad_fn=<AddBackward0>), tensor(7370.93115, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.491      0.316        0.3      0.172\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/79      5.82G     0.0418    0.03051    0.02482         54        640: 1\n",
      "tensor([10.53320], device='cuda:0', grad_fn=<AddBackward0>) tensor(21352.14258, device='cuda:0', grad_fn=<AddBackward0>), tensor(6794.38428, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.491      0.307      0.286      0.168\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/79      5.82G    0.04225    0.03126    0.02376         41        640: 1\n",
      "tensor([10.75020], device='cuda:0', grad_fn=<AddBackward0>) tensor(20677.48047, device='cuda:0', grad_fn=<AddBackward0>), tensor(7218.88916, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.581       0.32      0.312      0.186\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/79      5.82G    0.04145    0.03019    0.02443         45        640: 1\n",
      "tensor([11.10146], device='cuda:0', grad_fn=<AddBackward0>) tensor(23557.85156, device='cuda:0', grad_fn=<AddBackward0>), tensor(7215.69092, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.629      0.291      0.304      0.181\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/79      5.82G    0.04115    0.03085    0.02411         67        640: 1\n",
      "tensor([11.06944], device='cuda:0', grad_fn=<AddBackward0>) tensor(22604.51953, device='cuda:0', grad_fn=<AddBackward0>), tensor(7098.83936, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.616      0.288      0.319      0.188\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/79      5.82G    0.04152    0.03113    0.02471         61        640: 1\n",
      "tensor([11.33687], device='cuda:0', grad_fn=<AddBackward0>) tensor(23081.71094, device='cuda:0', grad_fn=<AddBackward0>), tensor(7459.76270, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.479      0.305      0.307      0.181\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/79      5.82G    0.04135    0.03027    0.02392         55        640: 1\n",
      "tensor([10.93085], device='cuda:0', grad_fn=<AddBackward0>) tensor(24066.77344, device='cuda:0', grad_fn=<AddBackward0>), tensor(6720.77393, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.62      0.316      0.326      0.193\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/79      5.82G    0.04139    0.03036     0.0238         34        640: 1\n",
      "tensor([10.56757], device='cuda:0', grad_fn=<AddBackward0>) tensor(22225.61914, device='cuda:0', grad_fn=<AddBackward0>), tensor(6847.20117, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.573      0.309       0.31      0.185\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/79      5.82G    0.04103    0.03074    0.02362         36        640: 1\n",
      "tensor([12.39163], device='cuda:0', grad_fn=<AddBackward0>) tensor(24900.95703, device='cuda:0', grad_fn=<AddBackward0>), tensor(8322.92285, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.564      0.316       0.31       0.18\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/79      5.82G    0.04158    0.03131    0.02363         52        640: 1\n",
      "tensor([11.26391], device='cuda:0', grad_fn=<AddBackward0>) tensor(23631.28320, device='cuda:0', grad_fn=<AddBackward0>), tensor(7253.89453, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.457      0.301        0.3       0.18\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/79      5.82G     0.0413    0.02997    0.02349         31        640: 1\n",
      "tensor([11.42693], device='cuda:0', grad_fn=<AddBackward0>) tensor(24701.38477, device='cuda:0', grad_fn=<AddBackward0>), tensor(7838.45068, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.431      0.305      0.313       0.19\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/79      5.82G    0.04086     0.0306    0.02338         49        640: 1\n",
      "tensor([9.88294], device='cuda:0', grad_fn=<AddBackward0>) tensor(21359.76172, device='cuda:0', grad_fn=<AddBackward0>), tensor(6255.69336, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.542      0.312      0.317      0.187\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/79      5.82G    0.04088     0.0297    0.02318         52        640: 1\n",
      "tensor([10.07568], device='cuda:0', grad_fn=<AddBackward0>) tensor(21353.05859, device='cuda:0', grad_fn=<AddBackward0>), tensor(6309.84570, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.533      0.303      0.302      0.175\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/79      5.82G    0.04158    0.03051    0.02416         41        640: 1\n",
      "tensor([9.95290], device='cuda:0', grad_fn=<AddBackward0>) tensor(22050.07227, device='cuda:0', grad_fn=<AddBackward0>), tensor(6062.05908, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.55        0.3      0.314      0.187\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/79      5.82G    0.04054    0.02952    0.02344         34        640: 1\n",
      "tensor([10.74333], device='cuda:0', grad_fn=<AddBackward0>) tensor(22248.66992, device='cuda:0', grad_fn=<AddBackward0>), tensor(7242.70215, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.555      0.304       0.31      0.183\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/79      5.82G    0.04033    0.02955      0.024         37        640: 1\n",
      "tensor([10.21246], device='cuda:0', grad_fn=<AddBackward0>) tensor(22133.30859, device='cuda:0', grad_fn=<AddBackward0>), tensor(6579.80957, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.601      0.291       0.31      0.184\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/79      5.82G    0.04025    0.03007    0.02362         33        640: 1\n",
      "tensor([10.33224], device='cuda:0', grad_fn=<AddBackward0>) tensor(22417.48438, device='cuda:0', grad_fn=<AddBackward0>), tensor(6432.98682, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.472      0.309      0.307      0.182\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/79      5.82G    0.04036    0.02888    0.02399         20        640: 1\n",
      "tensor([11.22738], device='cuda:0', grad_fn=<AddBackward0>) tensor(25804.52344, device='cuda:0', grad_fn=<AddBackward0>), tensor(7138.35107, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.507      0.307      0.315      0.186\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/79      5.82G    0.04048    0.03055    0.02313         49        640: 1\n",
      "tensor([10.72461], device='cuda:0', grad_fn=<AddBackward0>) tensor(22652.04688, device='cuda:0', grad_fn=<AddBackward0>), tensor(6783.26025, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.574      0.319      0.301      0.181\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/79      5.82G       0.04     0.0292    0.02347         93        640: 1\n",
      "tensor([10.41523], device='cuda:0', grad_fn=<AddBackward0>) tensor(21273.07617, device='cuda:0', grad_fn=<AddBackward0>), tensor(6204.78906, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.475        0.3      0.307      0.176\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/79      5.82G    0.03984    0.03017    0.02269         32        640: 1\n",
      "tensor([10.12901], device='cuda:0', grad_fn=<AddBackward0>) tensor(21124.64062, device='cuda:0', grad_fn=<AddBackward0>), tensor(6697.36816, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.492      0.308      0.312      0.181\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/79      5.82G    0.03974    0.02956    0.02381         47        640: 1\n",
      "tensor([11.02568], device='cuda:0', grad_fn=<AddBackward0>) tensor(21956.17383, device='cuda:0', grad_fn=<AddBackward0>), tensor(7115.70850, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.602      0.286      0.315      0.186\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/79      5.82G     0.0395    0.02968    0.02259         43        640: 1\n",
      "tensor([10.39601], device='cuda:0', grad_fn=<AddBackward0>) tensor(22439.55469, device='cuda:0', grad_fn=<AddBackward0>), tensor(6801.94531, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.631      0.286      0.318      0.193\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/79      5.82G    0.03941    0.02891    0.02245         37        640: 1\n",
      "tensor([10.31075], device='cuda:0', grad_fn=<AddBackward0>) tensor(23055.75000, device='cuda:0', grad_fn=<AddBackward0>), tensor(6601.50195, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.607      0.271      0.312      0.182\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/79      5.82G    0.03956    0.02969    0.02224         53        640: 1\n",
      "tensor([10.16000], device='cuda:0', grad_fn=<AddBackward0>) tensor(22697.26562, device='cuda:0', grad_fn=<AddBackward0>), tensor(6336.66260, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.469      0.312      0.312      0.185\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/79      5.82G    0.03954    0.03015    0.02271         59        640: 1\n",
      "tensor([10.75869], device='cuda:0', grad_fn=<AddBackward0>) tensor(22923.26562, device='cuda:0', grad_fn=<AddBackward0>), tensor(6657.86523, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.487      0.307      0.313      0.189\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/79      5.82G     0.0386    0.02829     0.0229         26        640: 1\n",
      "tensor([10.62461], device='cuda:0', grad_fn=<AddBackward0>) tensor(23051.42188, device='cuda:0', grad_fn=<AddBackward0>), tensor(7227.49951, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.49      0.305      0.313      0.187\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/79      5.82G    0.03943    0.02932    0.02238         54        640: 1\n",
      "tensor([10.42937], device='cuda:0', grad_fn=<AddBackward0>) tensor(22184.73242, device='cuda:0', grad_fn=<AddBackward0>), tensor(6569.36670, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.555      0.302       0.32      0.191\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/79      5.82G    0.03933    0.02927     0.0228         31        640: 1\n",
      "tensor([10.87811], device='cuda:0', grad_fn=<AddBackward0>) tensor(22768.24805, device='cuda:0', grad_fn=<AddBackward0>), tensor(7061.06104, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.587      0.307      0.314      0.184\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/79      5.82G     0.0393    0.02897    0.02227         56        640: 1\n",
      "tensor([10.95254], device='cuda:0', grad_fn=<AddBackward0>) tensor(23793.32617, device='cuda:0', grad_fn=<AddBackward0>), tensor(6915.69971, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.579      0.315      0.318      0.187\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/79      5.82G    0.03882    0.02921    0.02247         45        640: 1\n",
      "tensor([9.63866], device='cuda:0', grad_fn=<AddBackward0>) tensor(22640.10156, device='cuda:0', grad_fn=<AddBackward0>), tensor(6103.54590, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.552        0.3      0.315      0.187\n",
      "\n",
      "80 epochs completed in 1.032 hours.\n",
      "Optimizer stripped from runs/train/Lwf_with_head_openimages2/weights/last.pt, 14.9MB\n",
      "Optimizer stripped from runs/train/Lwf_with_head_openimages2/weights/best.pt, 14.9MB\n",
      "\n",
      "Validating runs/train/Lwf_with_head_openimages2/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.62      0.319      0.326      0.193\n",
      "                   car       1200        287      0.585      0.544      0.517      0.313\n",
      "                   van       1200         29       0.29      0.276       0.21      0.156\n",
      "                 truck       1200         29      0.245      0.448      0.202       0.13\n",
      "                person       1200       2264      0.408      0.323      0.287      0.134\n",
      "               bicycle       1200         54      0.405      0.389      0.357      0.195\n",
      "                  bird       1200        136      0.576      0.485      0.484       0.25\n",
      "                  boat       1200        145      0.492      0.372      0.359      0.162\n",
      "                bottle       1200         31          1          0          0          0\n",
      "                   bus       1200         15      0.516      0.641      0.737      0.525\n",
      "                   cat       1200          1          1          0     0.0452     0.0226\n",
      "                 chair       1200         21     0.0617      0.107     0.0377     0.0122\n",
      "                   dog       1200         42      0.856      0.426      0.525       0.29\n",
      "                 horse       1200         44       0.65      0.548      0.613      0.335\n",
      "                 sheep       1200         10      0.452        0.3      0.425      0.197\n",
      "             billboard       1200          4          1          0    0.00111   0.000556\n",
      "                rabbit       1200         11          1      0.207      0.448      0.274\n",
      "                monkey       1200         18      0.526      0.926      0.852      0.587\n",
      "                   pig       1200          6      0.802      0.667      0.684      0.437\n",
      "                   toy       1200         64      0.163     0.0469     0.0567     0.0199\n",
      "         traffic light       1200         18          1          0          0          0\n",
      "          traffic sign       1200          4          1          0    0.00723    0.00382\n",
      "Results saved to \u001b[1mruns/train/Lwf_with_head_openimages2\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : Lwf_with_head_openimages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/efd83894537a4d629a08bdc7386a58f4\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                    : 0.3967037350723689\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives       : 31.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5                : 0.357299582625485\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95            : 0.1951556475152966\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision             : 0.4048391059519835\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall                : 0.3888888888888889\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support               : 54\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives        : 21.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_f1                  : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_false_positives     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_mAP@.5              : 0.0011120215361869437\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_mAP@.5:.95          : 0.0005560107680934719\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_precision           : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_recall              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_support             : 4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_true_positives      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                       : 0.5265927098346973\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives          : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                   : 0.4841487603958254\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95               : 0.25035459760093143\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision                : 0.5755741439379883\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                   : 0.4852941176470588\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                  : 136\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives           : 66.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                       : 0.4239585111178857\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives          : 56.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                   : 0.35872921679918485\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95               : 0.16237536713638503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision                : 0.49206361566661816\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                   : 0.3724137931034483\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                  : 145\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives           : 54.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5                 : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall                 : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support                : 31\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                        : 0.5717619179309572\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives           : 9.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                    : 0.7365625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95                : 0.5254282363225439\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision                 : 0.5161233120154815\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                    : 0.640845723772553\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                   : 15\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives            : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                        : 0.5634555913297986\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives           : 111.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                    : 0.5174751861789864\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95                : 0.31289841818678554\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision                 : 0.5848699083471407\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                    : 0.5435540069686411\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                   : 287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives            : 156.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                    : 0.04522727272727273\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95                : 0.022613636363636364\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                    : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                      : 0.07823849370329082\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives         : 30.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                  : 0.03765267540012804\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95              : 0.012228282936957931\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision               : 0.061738916093270424\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                  : 0.10677344010677339\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support                 : 21\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives          : 2.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                        : 0.5689381954036175\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives           : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                    : 0.5249259871200312\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95                : 0.2903248858218051\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision                 : 0.8563620599880142\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                    : 0.4259687713886187\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                   : 42\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives            : 18.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                      : 0.5943134995742643\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives         : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                  : 0.6134663944178486\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95              : 0.3352694436807228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision               : 0.6495705223067699\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                  : 0.5477205495862212\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support                 : 44\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives          : 24.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2104]                   : (10.383932113647461, 45.18748474121094)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [160]         : (0.05289472670750823, 0.325713061540655)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [160]    : (0.024054806753663116, 0.19289001145843207)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [160]       : (0.4253875003562803, 0.8514413508944763)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [160]          : (0.046711532188138775, 0.3200330533503269)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_f1                     : 0.6710653472650663\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_false_positives        : 15.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_mAP@.5                 : 0.8519363618263637\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_mAP@.5:.95             : 0.5869296633781094\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_precision              : 0.5262156873429407\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_recall                 : 0.9259480068303597\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_support                : 18\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_true_positives         : 17.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                     : 0.36050223893583705\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives        : 1060.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5                 : 0.2871955257213892\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95             : 0.1335326491384455\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision              : 0.40804859039914526\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall                 : 0.32287985865724383\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support                : 2264\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives         : 731.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_f1                        : 0.7282908048713926\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_false_positives           : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_mAP@.5                    : 0.6842893940703606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_mAP@.5:.95                : 0.4370469766379236\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_precision                 : 0.8024678934253403\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_recall                    : 0.6666666666666666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_support                   : 6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_true_positives            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_f1                     : 0.34327215191328964\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_false_positives        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_mAP@.5                 : 0.44817592385218363\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_mAP@.5:.95             : 0.27390464871243936\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_precision              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_recall                 : 0.20719887838531906\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_support                : 11\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_true_positives         : 2.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                      : 0.3605403377601741\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives         : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                  : 0.4248240056653535\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95              : 0.1969837069251406\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision               : 0.4516923657051045\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                  : 0.3\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support                 : 10\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives          : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_f1                        : 0.07277928021144792\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_false_positives           : 15.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_mAP@.5                    : 0.056657876377984424\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_mAP@.5:.95                : 0.019944851766382173\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_precision                 : 0.16268057531215427\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_recall                    : 0.046875\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_support                   : 64\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_true_positives            : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_f1              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_mAP@.5          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_mAP@.5:.95      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_precision       : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_recall          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_support         : 18\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_true_positives  : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_f1               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_false_positives  : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_mAP@.5           : 0.007232793522267206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_mAP@.5:.95       : 0.00382089518668466\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_precision        : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_recall           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_support          : 4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_true_positives   : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [160]          : (0.03859647735953331, 0.07327460497617722)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [160]          : (0.022239642217755318, 0.053137023001909256)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [160]          : (0.02829003520309925, 0.039862338453531265)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_f1                      : 0.31672843126277733\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_false_positives         : 40.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5                  : 0.20160896254489027\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5:.95              : 0.1302772685331793\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_precision               : 0.24487065682034237\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_recall                  : 0.4482758620689655\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_support                 : 29\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_true_positives          : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [160]            : (0.05501778423786163, 0.06864698976278305)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [160]            : (0.03153581917285919, 0.0465218611061573)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [160]            : (0.024456726387143135, 0.02659960836172104)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_f1                        : 0.2829300483516228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_false_positives           : 20.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5                    : 0.21021322232136597\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5:.95                : 0.15563043858799708\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_precision                 : 0.29036973481417927\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_recall                    : 0.27586206896551724\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_support                   : 29\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_true_positives            : 8.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [160]                   : (0.00034750000000000026, 0.07011406844106464)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [160]                   : (0.00034750000000000026, 0.009740139416983522)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [160]                   : (0.00034750000000000026, 0.009740139416983522)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : Lwf_with_head_openimages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/efd83894537a4d629a08bdc7386a58f4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_enable          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : [0.0001, 0.001]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : ['./runs/train/increment_VOC_plain/weights/last.pt', './runs/train/fog_02/weights/last.pt']\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_openimages.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.225\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/openimages.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 80\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : Lwf_with_head_openimages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/Lwf_with_head_openimages2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/increment_VOC_plain/weights/last.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (2.25 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 325 metrics, params and output messages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_DER.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_openimages.yaml \\\n",
    "--data data/openimages.yaml \\\n",
    "--epochs 80 \\\n",
    "--weights ./runs/train/increment_VOC_plain/weights/last.pt \\\n",
    "--DER_enable \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda \\\n",
    "        1e-4 \\\n",
    "        1e-3 \\\n",
    "--Old_models \\\n",
    "        ./runs/train/increment_VOC_plain/weights/last.pt \\\n",
    "        ./runs/train/fog_02/weights/last.pt \\\n",
    "--name Lwf_with_head_openimages \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 这是没有数据回放的\n",
    "# 三个小时还是太离谱了一点\n",
    "# --DER_old_model \\\n",
    "#    ./runs/train/fog_02/weights/last.pt \\\n",
    "# 0.484, 0.59\n",
    "# 来个没有数据回放的，下一个执行它"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c24dbc1c-7031-4553-ab8d-2018abe0adf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/openimages.yaml, weights=['runs/train/Lwf_with_head_openimages/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/openimages/labels/test.cache... 600 ima\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        600       1621      0.651      0.163      0.153     0.0759\n",
      "                   car        600        113      0.228      0.478      0.256      0.133\n",
      "                   van        600          6          0          0    0.00321      0.001\n",
      "                 truck        600         17      0.233      0.294      0.204      0.125\n",
      "                person        600       1131      0.234      0.391      0.212     0.0881\n",
      "               bicycle        600         43      0.398      0.302       0.32      0.177\n",
      "                  bird        600         61      0.252      0.639      0.478      0.258\n",
      "                  boat        600         82      0.216      0.476      0.368      0.137\n",
      "                bottle        600          1          1          0          0          0\n",
      "                   bus        600          3          1          0      0.113     0.0794\n",
      "                   cat        600          5          1          0    0.00563    0.00273\n",
      "                 chair        600         12          0          0     0.0416     0.0186\n",
      "                   dog        600         25       0.33      0.178      0.241      0.141\n",
      "                 horse        600         37      0.427      0.649      0.531      0.235\n",
      "                 sheep        600          8          1      0.171      0.423      0.185\n",
      "                 train        600          2          1          0    0.00165    0.00116\n",
      "             billboard        600          3          1          0    0.00267    0.00191\n",
      "                rabbit        600          1          1          0    0.00299    0.00179\n",
      "                monkey        600         16          1          0     0.0734     0.0335\n",
      "                   pig        600          7          1          0      0.029     0.0189\n",
      "                   toy        600         42          1          0     0.0518     0.0297\n",
      "         traffic light        600          5          1          0    0.00168    0.00141\n",
      "          traffic sign        600          1          1          0    0.00467     0.0014\n",
      "Speed: 0.1ms pre-process, 2.8ms inference, 1.6ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp362\u001b[0m\n",
      "openimages\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/val_VOC.yaml, weights=['runs/train/Lwf_with_head_openimages/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.793      0.195      0.268      0.132\n",
      "                   car       4952       1201      0.554      0.803      0.767      0.457\n",
      "                person       4952       4528      0.468      0.547      0.508      0.242\n",
      "             aeroplane       4952        285          1          0     0.0491      0.018\n",
      "               bicycle       4952        337      0.541      0.582      0.584      0.319\n",
      "                  bird       4952        459       0.35      0.629      0.509      0.233\n",
      "                  boat       4952        263      0.178      0.634      0.397       0.17\n",
      "                bottle       4952        469          1          0      0.151     0.0556\n",
      "                   bus       4952        213          1          0      0.193      0.124\n",
      "                   cat       4952        358          1          0      0.096     0.0467\n",
      "                 chair       4952        756      0.919     0.0476      0.256      0.112\n",
      "                   cow       4952        244          1          0     0.0653     0.0298\n",
      "           diningtable       4952        206          1          0     0.0062      0.003\n",
      "                   dog       4952        489      0.789      0.046      0.374       0.18\n",
      "                 horse       4952        348      0.664      0.601      0.633      0.313\n",
      "             motorbike       4952        325          1          0     0.0204    0.00938\n",
      "           pottedplant       4952        480      0.404    0.00168      0.111     0.0385\n",
      "                 sheep       4952        242          1    0.00679      0.338      0.144\n",
      "                  sofa       4952        239          1          0     0.0154    0.00918\n",
      "                 train       4952        282          1          0     0.0583     0.0315\n",
      "             tvmonitor       4952        308          1          0      0.228      0.113\n",
      "Speed: 0.1ms pre-process, 1.5ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp363\u001b[0m\n",
      "Voc\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/val_kitti.yaml, weights=['runs/train/Lwf_with_head_openimages/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.703      0.141      0.162      0.073\n",
      "                   car       2244       8711      0.616      0.532      0.575      0.276\n",
      "                   van       2244        861      0.575     0.0283      0.112     0.0578\n",
      "                 truck       2244        333      0.102      0.024      0.025     0.0156\n",
      "                  tram       2244        138          1          0     0.0213     0.0103\n",
      "                person       2244       1286      0.334      0.546      0.412       0.18\n",
      "        person_sitting       2244         89          1          0     0.0115    0.00444\n",
      "               cyclist       2244        496          1          0      0.114     0.0306\n",
      "                  misc       2244        284          1          0     0.0236    0.00926\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp364\u001b[0m\n",
      "kitti\n"
     ]
    }
   ],
   "source": [
    "# 1e-4 1e-3\n",
    "model = f'runs/train/Lwf_with_head_openimages/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/openimages.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'openimages' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n",
    "# Voc\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/val_VOC.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Voc' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# kitti\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/val_kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'kitti' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "849e931b-d3a5-4cc1-9303-934155420c08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d11de8f-c6a2-46bb-a5ad-b1c59fd3650a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40929ebf-587a-45b2-bc48-d8a436e83541",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6875b3a4-017d-40b7-a229-9b3bd45a0e35",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ea4ecad-65f2-413c-9cba-00a3015ca9a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c46a3c4b-6957-47b0-bff8-4a7cce064011",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_DER: \u001b[0mweights=./runs/train/increment_VOC_plain/weights/last.pt, cfg=models/yolov5s_openimages.yaml, data=data/openimages_k_v.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=80, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=replay_Lwf_with_head_openimages, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[0.0001, 0.0005], Lwf_temperature=1.0, Old_models=['./runs/train/increment_VOC_plain/weights/last.pt', './runs/train/fog_02/weights/last.pt'], DER_enable=True, DER_old_model=[]\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/d736200f9aa848238e2064ec0f7ae379\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "首次创建 extractors\n",
      "成功拼接 extractors\n",
      "extractors共有模型个数： 1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1    110583  models.yolo_DerTest.Detect              [36, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "已知类别： 0\n",
      "YOLOv5s_openimages summary: 226 layers, 7230007 parameters, 7230007 gradients, 65.3 GFLOPs\n",
      "\n",
      "Transferred 342/723 items from runs/train/increment_VOC_plain/weights/last.pt\n",
      "Overriding model.yaml nc=36 with nc=26\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_DerTest.Detect              [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_openimages summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Overriding model.yaml nc=36 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_DerTest.Detect              [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_openimages summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 75 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old... 7701 images, 0\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/openimages/labels/val.cache... 1200 imag\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.23 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/replay_Lwf_with_head_openimages/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/replay_Lwf_with_head_openimages\u001b[0m\n",
      "Starting training for 80 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/79      3.72G    0.06111    0.03893    0.04387         29        640: 1\n",
      "tensor([11.37883], device='cuda:0', grad_fn=<AddBackward0>) tensor(17319.31250, device='cuda:0', grad_fn=<AddBackward0>), tensor(16598.90430, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.695      0.127      0.112      0.059\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/79      5.82G    0.05521    0.03428    0.03196         17        640: 1\n",
      "tensor([9.51993], device='cuda:0', grad_fn=<AddBackward0>) tensor(14646.36621, device='cuda:0', grad_fn=<AddBackward0>), tensor(13716.59570, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.582      0.217      0.194      0.109\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/79      5.83G    0.05054    0.03519    0.02806         50        640: 1\n",
      "tensor([8.64593], device='cuda:0', grad_fn=<AddBackward0>) tensor(14647.84082, device='cuda:0', grad_fn=<AddBackward0>), tensor(11864.83301, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.665      0.229      0.227      0.127\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/79      5.83G    0.04992    0.03572    0.02725         23        640: 1\n",
      "tensor([9.40947], device='cuda:0', grad_fn=<AddBackward0>) tensor(16182.88672, device='cuda:0', grad_fn=<AddBackward0>), tensor(13189.55762, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.608      0.256      0.252      0.151\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/79      5.83G    0.04739    0.03443    0.02598         22        640: 1\n",
      "tensor([9.55176], device='cuda:0', grad_fn=<AddBackward0>) tensor(17285.70898, device='cuda:0', grad_fn=<AddBackward0>), tensor(13475.60156, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.611      0.246       0.26      0.156\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/79      5.83G    0.04696    0.03469    0.02446         38        640: 1\n",
      "tensor([8.44901], device='cuda:0', grad_fn=<AddBackward0>) tensor(16821.25195, device='cuda:0', grad_fn=<AddBackward0>), tensor(11141.59277, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.534      0.312       0.28      0.172\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/79      5.83G    0.04657    0.03486    0.02391         22        640: 1\n",
      "tensor([9.34792], device='cuda:0', grad_fn=<AddBackward0>) tensor(18491.38672, device='cuda:0', grad_fn=<AddBackward0>), tensor(12829.08496, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.609      0.244      0.276       0.15\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/79      5.83G     0.0467    0.03404    0.02307         14        640: 1\n",
      "tensor([7.72817], device='cuda:0', grad_fn=<AddBackward0>) tensor(18744.64062, device='cuda:0', grad_fn=<AddBackward0>), tensor(9689.25781, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.519      0.304      0.279      0.166\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/79      5.83G    0.04516    0.03391    0.02232         30        640: 1\n",
      "tensor([8.59936], device='cuda:0', grad_fn=<AddBackward0>) tensor(16903.23438, device='cuda:0', grad_fn=<AddBackward0>), tensor(11668.05176, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.515      0.312      0.287      0.167\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/79      5.83G    0.04456    0.03391    0.02217         20        640: 1\n",
      "tensor([8.05231], device='cuda:0', grad_fn=<AddBackward0>) tensor(17827.90820, device='cuda:0', grad_fn=<AddBackward0>), tensor(10938.07031, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.531      0.294      0.297      0.182\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/79      5.83G    0.04371    0.03328    0.02099         18        640: 1\n",
      "tensor([7.31404], device='cuda:0', grad_fn=<AddBackward0>) tensor(18917.96289, device='cuda:0', grad_fn=<AddBackward0>), tensor(9567.59570, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.497      0.309      0.313      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/79      5.83G    0.04423    0.03405    0.02145         27        640: 1\n",
      "tensor([7.16971], device='cuda:0', grad_fn=<AddBackward0>) tensor(16871.99609, device='cuda:0', grad_fn=<AddBackward0>), tensor(8839.18359, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.465      0.322      0.303      0.185\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/79      5.83G    0.04369    0.03409    0.02056         36        640: 1\n",
      "tensor([8.47793], device='cuda:0', grad_fn=<AddBackward0>) tensor(19585.00391, device='cuda:0', grad_fn=<AddBackward0>), tensor(10888.30957, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.528      0.319      0.308      0.186\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/79      5.83G    0.04313    0.03247    0.02067         22        640: 1\n",
      "tensor([6.55971], device='cuda:0', grad_fn=<AddBackward0>) tensor(17323.99805, device='cuda:0', grad_fn=<AddBackward0>), tensor(7972.69629, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.599      0.299      0.318      0.192\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/79      5.83G    0.04394    0.03287    0.02128         13        640: 1\n",
      "tensor([8.29056], device='cuda:0', grad_fn=<AddBackward0>) tensor(19461.01562, device='cuda:0', grad_fn=<AddBackward0>), tensor(10722.55078, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.498       0.35      0.308      0.194\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/79      5.83G    0.04241    0.03296    0.02045         30        640: 1\n",
      "tensor([6.80822], device='cuda:0', grad_fn=<AddBackward0>) tensor(18888.78320, device='cuda:0', grad_fn=<AddBackward0>), tensor(8091.94922, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.565      0.311      0.314      0.198\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/79      5.83G    0.04239    0.03327    0.01997         31        640: 1\n",
      "tensor([6.33743], device='cuda:0', grad_fn=<AddBackward0>) tensor(19953.49414, device='cuda:0', grad_fn=<AddBackward0>), tensor(7207.37451, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.589      0.312      0.317      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/79      5.83G    0.04257    0.03365    0.01911         38        640: 1\n",
      "tensor([7.05095], device='cuda:0', grad_fn=<AddBackward0>) tensor(18805.23828, device='cuda:0', grad_fn=<AddBackward0>), tensor(8604.25879, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.543      0.343       0.33        0.2\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/79      5.83G    0.04211    0.03287    0.01888         47        640: 1\n",
      "tensor([7.87065], device='cuda:0', grad_fn=<AddBackward0>) tensor(18673.57812, device='cuda:0', grad_fn=<AddBackward0>), tensor(9633.19629, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.58      0.309      0.335      0.204\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/79      5.83G    0.04175    0.03203    0.01946         30        640: 1\n",
      "tensor([7.00411], device='cuda:0', grad_fn=<AddBackward0>) tensor(17658.04883, device='cuda:0', grad_fn=<AddBackward0>), tensor(8650.03125, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.555      0.299       0.33      0.202\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/79      5.83G    0.04168     0.0326    0.01848         22        640: 1\n",
      "tensor([7.04316], device='cuda:0', grad_fn=<AddBackward0>) tensor(19936.82812, device='cuda:0', grad_fn=<AddBackward0>), tensor(8419.95801, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.515      0.315      0.305      0.182\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/79      5.83G    0.04115    0.03217    0.01828         34        640: 1\n",
      "tensor([6.69973], device='cuda:0', grad_fn=<AddBackward0>) tensor(17482.11328, device='cuda:0', grad_fn=<AddBackward0>), tensor(8001.03906, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.553      0.318      0.329      0.201\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/79      5.83G    0.04063    0.03232    0.01791         21        640: 1\n",
      "tensor([6.55092], device='cuda:0', grad_fn=<AddBackward0>) tensor(17115.86328, device='cuda:0', grad_fn=<AddBackward0>), tensor(8324.29004, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.546      0.317      0.337      0.205\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/79      5.83G    0.04128    0.03251    0.01857         31        640: 1\n",
      "tensor([6.80740], device='cuda:0', grad_fn=<AddBackward0>) tensor(19574.78320, device='cuda:0', grad_fn=<AddBackward0>), tensor(7493.67871, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.501       0.36      0.348      0.222\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/79      5.83G    0.04078    0.03305    0.01831         28        640: 1\n",
      "tensor([7.83062], device='cuda:0', grad_fn=<AddBackward0>) tensor(17977.98633, device='cuda:0', grad_fn=<AddBackward0>), tensor(9968.25195, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.545      0.318      0.328      0.208\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/79      5.83G    0.04118    0.03284     0.0177         40        640: 1\n",
      "tensor([7.47253], device='cuda:0', grad_fn=<AddBackward0>) tensor(17386.23242, device='cuda:0', grad_fn=<AddBackward0>), tensor(9084.69141, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.542      0.308      0.341      0.211\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/79      5.83G    0.04038    0.03241    0.01761         37        640: 1\n",
      "tensor([7.10181], device='cuda:0', grad_fn=<AddBackward0>) tensor(19309.83398, device='cuda:0', grad_fn=<AddBackward0>), tensor(8356.79785, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.564      0.319       0.35      0.218\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/79      5.83G    0.03995    0.03188    0.01749         31        640: 1\n",
      "tensor([7.71362], device='cuda:0', grad_fn=<AddBackward0>) tensor(19612.30078, device='cuda:0', grad_fn=<AddBackward0>), tensor(9899.78125, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.531      0.339      0.341      0.211\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/79      5.83G    0.03973     0.0325    0.01689         25        640: 1\n",
      "tensor([6.28248], device='cuda:0', grad_fn=<AddBackward0>) tensor(17631.11133, device='cuda:0', grad_fn=<AddBackward0>), tensor(7443.86426, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.524       0.35      0.344      0.217\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/79      5.83G    0.03931    0.03153    0.01699         22        640: 1\n",
      "tensor([6.96369], device='cuda:0', grad_fn=<AddBackward0>) tensor(19254.88672, device='cuda:0', grad_fn=<AddBackward0>), tensor(8753.10156, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.534      0.366      0.342      0.225\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/79      5.83G    0.04016    0.03229    0.01828         21        640: 1\n",
      "tensor([8.11944], device='cuda:0', grad_fn=<AddBackward0>) tensor(19842.18945, device='cuda:0', grad_fn=<AddBackward0>), tensor(9953.66797, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.576      0.312      0.346      0.219\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/79      5.83G    0.03931    0.03095    0.01691         32        640: 1\n",
      "tensor([7.23775], device='cuda:0', grad_fn=<AddBackward0>) tensor(18372.09375, device='cuda:0', grad_fn=<AddBackward0>), tensor(8906.41211, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.529      0.383      0.352       0.22\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/79      5.83G     0.0394    0.03193    0.01681         26        640: 1\n",
      "tensor([6.66946], device='cuda:0', grad_fn=<AddBackward0>) tensor(19390.82812, device='cuda:0', grad_fn=<AddBackward0>), tensor(8089.19238, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.53      0.379      0.356      0.226\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/79      5.83G    0.03923    0.03118    0.01757         22        640: 1\n",
      "tensor([7.34686], device='cuda:0', grad_fn=<AddBackward0>) tensor(19694.92969, device='cuda:0', grad_fn=<AddBackward0>), tensor(9001.37012, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.528      0.369      0.351      0.228\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/79      5.83G    0.03882    0.03098    0.01649         25        640: 1\n",
      "tensor([6.92798], device='cuda:0', grad_fn=<AddBackward0>) tensor(18940.19922, device='cuda:0', grad_fn=<AddBackward0>), tensor(8479.47363, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.526      0.372      0.365       0.24\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/79      5.83G    0.03815    0.03083    0.01605         21        640: 1\n",
      "tensor([6.50623], device='cuda:0', grad_fn=<AddBackward0>) tensor(19481.48633, device='cuda:0', grad_fn=<AddBackward0>), tensor(7810.04834, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.514      0.355      0.352      0.231\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/79      5.83G    0.03863    0.03164    0.01648         42        640: 1\n",
      "tensor([6.33286], device='cuda:0', grad_fn=<AddBackward0>) tensor(17472.64062, device='cuda:0', grad_fn=<AddBackward0>), tensor(7485.91406, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.501       0.36      0.353      0.231\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/79      5.83G    0.03805    0.03057    0.01613         22        640: 1\n",
      "tensor([7.10342], device='cuda:0', grad_fn=<AddBackward0>) tensor(18477.76562, device='cuda:0', grad_fn=<AddBackward0>), tensor(8866.78809, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.538      0.388      0.373      0.239\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/79      5.83G    0.03818    0.03086      0.016         27        640: 1\n",
      "tensor([8.60156], device='cuda:0', grad_fn=<AddBackward0>) tensor(22078.69531, device='cuda:0', grad_fn=<AddBackward0>), tensor(10844.87500, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.545      0.367      0.363      0.241\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/79      5.83G    0.03751    0.03084    0.01564         41        640: 1\n",
      "tensor([7.34785], device='cuda:0', grad_fn=<AddBackward0>) tensor(17905.17188, device='cuda:0', grad_fn=<AddBackward0>), tensor(9523.60547, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.491      0.371      0.346      0.224\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/79      5.83G    0.03785    0.03152    0.01598         28        640: 1\n",
      "tensor([7.53496], device='cuda:0', grad_fn=<AddBackward0>) tensor(18077.49414, device='cuda:0', grad_fn=<AddBackward0>), tensor(9691.56445, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.512      0.392      0.364      0.239\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/79      5.83G    0.03753    0.03069    0.01572         29        640: 1\n",
      "tensor([6.79259], device='cuda:0', grad_fn=<AddBackward0>) tensor(17176.23438, device='cuda:0', grad_fn=<AddBackward0>), tensor(8700.27344, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.531      0.383      0.363      0.236\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/79      5.83G    0.03712    0.03046    0.01586         21        640: 1\n",
      "tensor([6.90982], device='cuda:0', grad_fn=<AddBackward0>) tensor(18842.00977, device='cuda:0', grad_fn=<AddBackward0>), tensor(8523.28516, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.512      0.381      0.356       0.23\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/79      5.83G    0.03714    0.03025    0.01584         29        640: 1\n",
      "tensor([6.06594], device='cuda:0', grad_fn=<AddBackward0>) tensor(18763.74805, device='cuda:0', grad_fn=<AddBackward0>), tensor(6775.73975, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.506      0.381       0.36      0.235\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/79      5.83G    0.03731    0.03147    0.01607         39        640: 1\n",
      "tensor([7.36475], device='cuda:0', grad_fn=<AddBackward0>) tensor(18584.69727, device='cuda:0', grad_fn=<AddBackward0>), tensor(8522.83984, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.538      0.367      0.364      0.239\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/79      5.83G    0.03714    0.03026    0.01546         31        640: 1\n",
      "tensor([7.02023], device='cuda:0', grad_fn=<AddBackward0>) tensor(20278.40039, device='cuda:0', grad_fn=<AddBackward0>), tensor(8090.34424, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.517      0.381      0.363       0.24\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/79      5.83G    0.03764    0.03042    0.01548         45        640: 1\n",
      "tensor([8.13503], device='cuda:0', grad_fn=<AddBackward0>) tensor(19894.43945, device='cuda:0', grad_fn=<AddBackward0>), tensor(9713.71387, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.537      0.387      0.374      0.248\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/79      5.83G     0.0372    0.02997      0.015         19        640: 1\n",
      "tensor([5.85520], device='cuda:0', grad_fn=<AddBackward0>) tensor(17807.89258, device='cuda:0', grad_fn=<AddBackward0>), tensor(6924.98730, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.555      0.399      0.386      0.251\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/79      5.83G    0.03655    0.02944    0.01522         27        640: 1\n",
      "tensor([6.64751], device='cuda:0', grad_fn=<AddBackward0>) tensor(18728.07617, device='cuda:0', grad_fn=<AddBackward0>), tensor(7896.97510, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.562      0.374      0.389      0.258\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/79      5.83G    0.03657    0.02992     0.0151         40        640: 1\n",
      "tensor([6.89729], device='cuda:0', grad_fn=<AddBackward0>) tensor(17216.93555, device='cuda:0', grad_fn=<AddBackward0>), tensor(8323.33887, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.561        0.4      0.385      0.253\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/79      5.83G    0.03647    0.02958    0.01478         40        640: 1\n",
      "tensor([7.02294], device='cuda:0', grad_fn=<AddBackward0>) tensor(18368.18359, device='cuda:0', grad_fn=<AddBackward0>), tensor(8758.82422, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.546      0.403      0.389      0.258\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/79      5.83G    0.03626    0.03002     0.0147         67        640: 1\n",
      "tensor([6.11205], device='cuda:0', grad_fn=<AddBackward0>) tensor(16712.89062, device='cuda:0', grad_fn=<AddBackward0>), tensor(6763.85449, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.575      0.363      0.386      0.255\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/79      5.83G    0.03628    0.03005    0.01475         30        640: 1\n",
      "tensor([7.72619], device='cuda:0', grad_fn=<AddBackward0>) tensor(19589.20312, device='cuda:0', grad_fn=<AddBackward0>), tensor(9743.14941, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.542      0.381      0.383      0.251\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/79      5.83G    0.03615    0.03036    0.01477         35        640: 1\n",
      "tensor([6.53305], device='cuda:0', grad_fn=<AddBackward0>) tensor(18805.13672, device='cuda:0', grad_fn=<AddBackward0>), tensor(7609.54443, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.58      0.342      0.375      0.245\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/79      5.83G    0.03586    0.02958    0.01475         28        640: 1\n",
      "tensor([7.80345], device='cuda:0', grad_fn=<AddBackward0>) tensor(20463.27734, device='cuda:0', grad_fn=<AddBackward0>), tensor(9807.68555, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233        0.6      0.349       0.39      0.248\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/79      5.83G    0.03565    0.02971    0.01487         23        640: 1\n",
      "tensor([6.85852], device='cuda:0', grad_fn=<AddBackward0>) tensor(17169.87695, device='cuda:0', grad_fn=<AddBackward0>), tensor(8766.32324, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.533      0.379      0.382       0.25\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/79      5.83G    0.03553    0.02949    0.01419         50        640: 1\n",
      "tensor([6.01336], device='cuda:0', grad_fn=<AddBackward0>) tensor(18315.10547, device='cuda:0', grad_fn=<AddBackward0>), tensor(6539.19629, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.558       0.38      0.373      0.248\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/79      5.83G     0.0358     0.0294    0.01418         39        640: 1\n",
      "tensor([5.52258], device='cuda:0', grad_fn=<AddBackward0>) tensor(15344.11621, device='cuda:0', grad_fn=<AddBackward0>), tensor(6437.33594, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.527      0.381      0.367      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/79      5.83G    0.03542    0.02974    0.01395         40        640: 1\n",
      "tensor([6.09383], device='cuda:0', grad_fn=<AddBackward0>) tensor(18314.55273, device='cuda:0', grad_fn=<AddBackward0>), tensor(6863.85938, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.593      0.371       0.38      0.254\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/79      5.83G    0.03527    0.02894    0.01415         23        640: 1\n",
      "tensor([7.11218], device='cuda:0', grad_fn=<AddBackward0>) tensor(19057.98047, device='cuda:0', grad_fn=<AddBackward0>), tensor(8986.39258, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.486      0.398       0.39       0.26\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/79      5.83G    0.03542    0.02938    0.01414         41        640: 1\n",
      "tensor([6.76625], device='cuda:0', grad_fn=<AddBackward0>) tensor(18688.81641, device='cuda:0', grad_fn=<AddBackward0>), tensor(7871.15820, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.504      0.384      0.379       0.25\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/79      5.83G    0.03532    0.02868    0.01407         47        640: 1\n",
      "tensor([7.43558], device='cuda:0', grad_fn=<AddBackward0>) tensor(20852.62109, device='cuda:0', grad_fn=<AddBackward0>), tensor(8522.84375, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.552      0.392      0.377      0.253\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/79      5.83G    0.03506    0.02902    0.01384         39        640: 1\n",
      "tensor([5.95326], device='cuda:0', grad_fn=<AddBackward0>) tensor(16895.20312, device='cuda:0', grad_fn=<AddBackward0>), tensor(6885.34424, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.55      0.394      0.383      0.253\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/79      5.83G    0.03487    0.02869    0.01372         33        640: 1\n",
      "tensor([6.23803], device='cuda:0', grad_fn=<AddBackward0>) tensor(18446.40234, device='cuda:0', grad_fn=<AddBackward0>), tensor(7120.57422, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.561      0.395      0.388      0.259\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/79      5.83G    0.03435    0.02819    0.01367         32        640: 1\n",
      "tensor([5.84977], device='cuda:0', grad_fn=<AddBackward0>) tensor(19374.45508, device='cuda:0', grad_fn=<AddBackward0>), tensor(6336.04346, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.554      0.387      0.389      0.261\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/79      5.83G    0.03425    0.02861    0.01371         39        640: 1\n",
      "tensor([5.91279], device='cuda:0', grad_fn=<AddBackward0>) tensor(19877.77148, device='cuda:0', grad_fn=<AddBackward0>), tensor(6300.32080, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.563      0.376      0.398      0.265\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/79      5.83G    0.03396    0.02819    0.01353         33        640: 1\n",
      "tensor([5.68239], device='cuda:0', grad_fn=<AddBackward0>) tensor(16898.47266, device='cuda:0', grad_fn=<AddBackward0>), tensor(6568.73975, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.503       0.39      0.392      0.262\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/79      5.83G    0.03394    0.02775    0.01372         22        640: 1\n",
      "tensor([6.80947], device='cuda:0', grad_fn=<AddBackward0>) tensor(18613.28125, device='cuda:0', grad_fn=<AddBackward0>), tensor(8725.08301, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.501      0.387      0.391      0.262\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/79      5.83G    0.03355     0.0281    0.01348         26        640: 1\n",
      "tensor([6.35329], device='cuda:0', grad_fn=<AddBackward0>) tensor(18971.83594, device='cuda:0', grad_fn=<AddBackward0>), tensor(7458.08350, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.511      0.397      0.393      0.262\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/79      5.83G    0.03339    0.02758    0.01336         20        640: 1\n",
      "tensor([6.32691], device='cuda:0', grad_fn=<AddBackward0>) tensor(18289.54492, device='cuda:0', grad_fn=<AddBackward0>), tensor(7829.00977, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233       0.57      0.377      0.391       0.26\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/79      5.83G    0.03357    0.02791    0.01322         28        640: 1\n",
      "tensor([6.77910], device='cuda:0', grad_fn=<AddBackward0>) tensor(19370.56055, device='cuda:0', grad_fn=<AddBackward0>), tensor(8074.89453, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.574      0.378        0.4      0.268\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/79      5.83G    0.03348    0.02805    0.01332         29        640: 1\n",
      "tensor([6.91316], device='cuda:0', grad_fn=<AddBackward0>) tensor(19172.32422, device='cuda:0', grad_fn=<AddBackward0>), tensor(8558.09668, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.573      0.386      0.396      0.267\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/79      5.83G    0.03364    0.02837    0.01331         30        640: 1\n",
      "tensor([6.46457], device='cuda:0', grad_fn=<AddBackward0>) tensor(18172.03516, device='cuda:0', grad_fn=<AddBackward0>), tensor(7501.70312, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.614      0.381      0.399      0.267\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/79      5.83G    0.03346    0.02795    0.01332         34        640: 1\n",
      "tensor([5.89503], device='cuda:0', grad_fn=<AddBackward0>) tensor(18333.50195, device='cuda:0', grad_fn=<AddBackward0>), tensor(6697.46387, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.548       0.39      0.393      0.261\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/79      5.83G    0.03311    0.02847    0.01291         38        640: 1\n",
      "tensor([5.64936], device='cuda:0', grad_fn=<AddBackward0>) tensor(17388.57227, device='cuda:0', grad_fn=<AddBackward0>), tensor(6275.97852, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.577      0.365      0.395      0.263\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/79      5.83G    0.03325    0.02694    0.01339         20        640: 1\n",
      "tensor([6.88700], device='cuda:0', grad_fn=<AddBackward0>) tensor(20057.04102, device='cuda:0', grad_fn=<AddBackward0>), tensor(8254.66797, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.499      0.394      0.391      0.261\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/79      5.83G    0.03289    0.02713     0.0126         26        640: 1\n",
      "tensor([5.36792], device='cuda:0', grad_fn=<AddBackward0>) tensor(16974.67578, device='cuda:0', grad_fn=<AddBackward0>), tensor(6037.55713, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.583       0.37      0.393      0.264\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/79      5.83G    0.03351    0.02778    0.01328         30        640: 1\n",
      "tensor([6.32196], device='cuda:0', grad_fn=<AddBackward0>) tensor(18636.10742, device='cuda:0', grad_fn=<AddBackward0>), tensor(6976.28223, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.585      0.371      0.392       0.26\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/79      5.83G    0.03321    0.02852    0.01297         56        640: 1\n",
      "tensor([5.98382], device='cuda:0', grad_fn=<AddBackward0>) tensor(17542.98438, device='cuda:0', grad_fn=<AddBackward0>), tensor(6344.09619, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.587      0.365      0.394      0.263\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/79      5.83G    0.03272    0.02796    0.01249         50        640: 1\n",
      "tensor([6.12530], device='cuda:0', grad_fn=<AddBackward0>) tensor(16761.49023, device='cuda:0', grad_fn=<AddBackward0>), tensor(7238.27734, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.589      0.372      0.395      0.264\n",
      "\n",
      "80 epochs completed in 1.726 hours.\n",
      "Optimizer stripped from runs/train/replay_Lwf_with_head_openimages/weights/last.pt, 14.9MB\n",
      "Optimizer stripped from runs/train/replay_Lwf_with_head_openimages/weights/best.pt, 14.9MB\n",
      "\n",
      "Validating runs/train/replay_Lwf_with_head_openimages/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1200       3233      0.573      0.377        0.4      0.268\n",
      "                   car       1200        287      0.675      0.585      0.557      0.366\n",
      "                   van       1200         29      0.449      0.281      0.329      0.256\n",
      "                 truck       1200         29      0.319      0.379      0.272      0.162\n",
      "                person       1200       2264      0.467       0.34      0.313      0.158\n",
      "               bicycle       1200         54      0.561      0.407      0.467      0.284\n",
      "                  bird       1200        136      0.635      0.647      0.571       0.34\n",
      "                  boat       1200        145      0.615      0.414      0.455       0.24\n",
      "                bottle       1200         31          0          0    0.00105   0.000402\n",
      "                   bus       1200         15      0.541      0.733      0.775      0.609\n",
      "                   cat       1200          1          1          0     0.0355     0.0213\n",
      "                 chair       1200         21      0.211      0.333      0.173     0.0831\n",
      "                   dog       1200         42      0.661      0.557      0.626      0.414\n",
      "                 horse       1200         44      0.708      0.591      0.684      0.444\n",
      "                 sheep       1200         10      0.515        0.6        0.5      0.272\n",
      "             billboard       1200          4          0          0     0.0628     0.0251\n",
      "                rabbit       1200         11      0.984      0.364      0.629      0.463\n",
      "                monkey       1200         18      0.721      0.944      0.923      0.692\n",
      "                   pig       1200          6      0.811      0.667      0.686      0.553\n",
      "                   toy       1200         64      0.161     0.0752     0.0633     0.0273\n",
      "         traffic light       1200         18          1          0     0.0261     0.0104\n",
      "          traffic sign       1200          4          1          0       0.25        0.2\n",
      "Results saved to \u001b[1mruns/train/replay_Lwf_with_head_openimages\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : replay_Lwf_with_head_openimages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/d736200f9aa848238e2064ec0f7ae379\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                    : 0.471949373982314\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives       : 17.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5                : 0.4674523982885265\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95            : 0.2844214822976921\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision             : 0.560790467580591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall                : 0.4074074074074074\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support               : 54\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives        : 22.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_f1                  : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_false_positives     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_mAP@.5              : 0.06281378600823044\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_mAP@.5:.95          : 0.025125514403292176\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_precision           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_recall              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_support             : 4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     billboard_true_positives      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                       : 0.6407208841849269\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives          : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                   : 0.5713852529346397\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95               : 0.3404754919727861\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision                : 0.6345059006841708\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                   : 0.6470588235294118\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                  : 136\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives           : 88.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                       : 0.494648653955494\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives          : 38.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                   : 0.45482178182860966\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95               : 0.2398397990484245\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision                : 0.6147765544278885\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                   : 0.41379310344827586\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                  : 145\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives           : 60.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5                 : 0.0010458983411641295\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95             : 0.00040218485159751303\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall                 : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support                : 31\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                        : 0.6224918488781662\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives           : 9.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                    : 0.7747225436280127\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95                : 0.6092648926458263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision                 : 0.5407576877340889\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                    : 0.7333333333333333\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                   : 15\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives            : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                        : 0.6269362745482029\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives           : 81.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                    : 0.5573592385093098\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95                : 0.366104305434263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision                 : 0.6748623938818408\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                    : 0.5853658536585366\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                   : 287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives            : 168.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                    : 0.035535714285714275\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95                : 0.021321428571428564\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                    : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                      : 0.2581170041257775\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives         : 26.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                  : 0.17314265594790215\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95              : 0.08314306539908957\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision               : 0.210596187597262\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                  : 0.3333333333333333\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support                 : 21\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives          : 7.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                        : 0.6044115232248558\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives           : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                    : 0.625531090563344\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95                : 0.4136717427780364\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision                 : 0.6608399221732555\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                    : 0.5568617101950435\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                   : 42\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives            : 23.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                      : 0.6439923557010742\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives         : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                  : 0.6838996329475131\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95              : 0.4439109730802605\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision               : 0.7075542284889563\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                  : 0.5909090909090909\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support                 : 44\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives          : 26.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [3848]                   : (6.536289215087891, 29.06185531616211)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [160]         : (0.11170634257226604, 0.39988693212275156)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [160]    : (0.05902362153141277, 0.2677704499769531)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [160]       : (0.465001120474404, 0.6953072901068925)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [160]          : (0.12651036925457118, 0.40346722221242975)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_f1                     : 0.8174854714194607\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_false_positives        : 7.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_mAP@.5                 : 0.923361111111111\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_mAP@.5:.95             : 0.6916576999999999\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_precision              : 0.7206152223405502\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_recall                 : 0.9444444444444444\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_support                : 18\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     monkey_true_positives         : 17.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                     : 0.3934341658780844\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives        : 880.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5                 : 0.3127699301600865\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95             : 0.1578472179918722\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision              : 0.4665957430611849\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall                 : 0.3401060070671378\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support                : 2264\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives         : 770.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_f1                        : 0.7315951273510827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_false_positives           : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_mAP@.5                    : 0.6862748538011696\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_mAP@.5:.95                : 0.5531850877192983\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_precision                 : 0.810535345856215\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_recall                    : 0.6666666666666666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_support                   : 6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pig_true_positives            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_f1                     : 0.5310394887800028\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_false_positives        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_mAP@.5                 : 0.6290182926829269\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_mAP@.5:.95             : 0.46344879727322547\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_precision              : 0.9840599387266054\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_recall                 : 0.36363636363636365\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_support                : 11\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rabbit_true_positives         : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                      : 0.5542339528275761\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives         : 6.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                  : 0.4995604395604395\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95              : 0.2724099462365591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision               : 0.5149548712767104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                  : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support                 : 10\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives          : 6.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_f1                        : 0.10259273054436929\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_false_positives           : 26.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_mAP@.5                    : 0.06326997777711721\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_mAP@.5:.95                : 0.027285031999130828\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_precision                 : 0.16131282797949462\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_recall                    : 0.0752138502138502\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_support                   : 64\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     toy_true_positives            : 5.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_f1              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_mAP@.5          : 0.026119262295081967\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_mAP@.5:.95      : 0.010447704918032787\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_precision       : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_recall          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_support         : 18\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic light_true_positives  : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_f1               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_false_positives  : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_mAP@.5           : 0.24970779220779218\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_mAP@.5:.95       : 0.19962445887445884\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_precision        : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_recall           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_support          : 4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     traffic sign_true_positives   : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [160]          : (0.03272123262286186, 0.06110526993870735)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [160]          : (0.01248817890882492, 0.043866101652383804)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [160]          : (0.02694232203066349, 0.03893159329891205)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_f1                      : 0.34660973236365217\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_false_positives         : 23.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5                  : 0.27156023191179424\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5:.95              : 0.16180698279269837\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_precision               : 0.31909990444848835\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_recall                  : 0.3793103448275862\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_support                 : 29\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_true_positives          : 11.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [160]            : (0.05223755165934563, 0.06319120526313782)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [160]            : (0.0266847163438797, 0.0374559722840786)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [160]            : (0.023791437968611717, 0.027075065299868584)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_f1                        : 0.3460643321124904\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_false_positives           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5                    : 0.32872626288897594\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5:.95                : 0.2562004688090326\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_precision                 : 0.4491838600567212\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_recall                    : 0.2814513108255126\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_support                   : 29\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_true_positives            : 8.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [160]                   : (0.00034750000000000026, 0.07006224066390042)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [160]                   : (0.00034750000000000026, 0.009745755532503456)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [160]                   : (0.00034750000000000026, 0.009745755532503456)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : replay_Lwf_with_head_openimages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/d736200f9aa848238e2064ec0f7ae379\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_enable          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : [0.0001, 0.0005]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : ['./runs/train/increment_VOC_plain/weights/last.pt', './runs/train/fog_02/weights/last.pt']\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_openimages.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.225\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/openimages_k_v.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 80\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : replay_Lwf_with_head_openimages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/replay_Lwf_with_head_openimages\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/increment_VOC_plain/weights/last.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (2.33 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_DER.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_openimages.yaml \\\n",
    "--data data/openimages_k_v.yaml \\\n",
    "--epochs 80 \\\n",
    "--weights ./runs/train/increment_VOC_plain/weights/last.pt \\\n",
    "--DER_enable \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda \\\n",
    "        1e-4 \\\n",
    "        5e-4 \\\n",
    "--Old_models \\\n",
    "        ./runs/train/increment_VOC_plain/weights/last.pt \\\n",
    "        ./runs/train/fog_02/weights/last.pt \\\n",
    "--name replay_Lwf_with_head_openimages \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 这是没有数据回放的\n",
    "# 三个小时还是太离谱了一点\n",
    "# --DER_old_model \\\n",
    "#    ./runs/train/fog_02/weights/last.pt \\\n",
    "# 0.484, 0.59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76f7251b-c677-4f12-a716-c55c57d7bb1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/openimages.yaml, weights=['runs/train/replay_Lwf_with_head_openimages/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/openimages/labels/test.cache... 600 ima\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        600       1621      0.442      0.432      0.389      0.251\n",
      "                   car        600        113      0.457      0.476      0.311      0.208\n",
      "                   van        600          6          0          0     0.0199     0.0134\n",
      "                 truck        600         17      0.738      0.499      0.561      0.474\n",
      "                person        600       1131      0.462      0.349      0.308      0.157\n",
      "               bicycle        600         43      0.634      0.442      0.533       0.32\n",
      "                  bird        600         61      0.478      0.557      0.498      0.319\n",
      "                  boat        600         82      0.615      0.488       0.48      0.257\n",
      "                bottle        600          1          0          0          0          0\n",
      "                   bus        600          3      0.302      0.333      0.336      0.302\n",
      "                   cat        600          5      0.578        0.6      0.648      0.335\n",
      "                 chair        600         12      0.204       0.25      0.242      0.143\n",
      "                   dog        600         25      0.685       0.88      0.692      0.465\n",
      "                 horse        600         37      0.641      0.757      0.714      0.416\n",
      "                 sheep        600          8       0.48      0.625       0.65      0.528\n",
      "                 train        600          2          0          0    0.00336   0.000336\n",
      "             billboard        600          3          0          0      0.121     0.0971\n",
      "                rabbit        600          1      0.481      0.961      0.497      0.448\n",
      "                monkey        600         16      0.556      0.625      0.594      0.354\n",
      "                   pig        600          7       0.34      0.429      0.457       0.35\n",
      "                   toy        600         42      0.579      0.262      0.272      0.129\n",
      "         traffic light        600          5          1          0      0.114     0.0682\n",
      "          traffic sign        600          1      0.489      0.977      0.497      0.145\n",
      "Speed: 0.1ms pre-process, 2.9ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp355\u001b[0m\n",
      "openimages\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/val_VOC.yaml, weights=['runs/train/replay_Lwf_with_head_openimages/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719      0.617      0.672      0.407\n",
      "                   car       4952       1201      0.784      0.816      0.822      0.579\n",
      "                person       4952       4528      0.723      0.672      0.724      0.418\n",
      "             aeroplane       4952        285      0.882      0.463      0.697      0.396\n",
      "               bicycle       4952        337      0.803       0.73      0.806      0.506\n",
      "                  bird       4952        459      0.735      0.601      0.641      0.359\n",
      "                  boat       4952        263      0.442       0.54      0.473      0.239\n",
      "                bottle       4952        469      0.644      0.488      0.504      0.288\n",
      "                   bus       4952        213      0.776      0.676      0.765      0.568\n",
      "                   cat       4952        358       0.76      0.673      0.733      0.431\n",
      "                 chair       4952        756      0.643      0.444      0.492      0.277\n",
      "                   cow       4952        244      0.672      0.717      0.738      0.474\n",
      "           diningtable       4952        206      0.748      0.491        0.6      0.329\n",
      "                   dog       4952        489      0.767       0.62      0.722      0.423\n",
      "                 horse       4952        348      0.833      0.744      0.823      0.541\n",
      "             motorbike       4952        325      0.773      0.668      0.758      0.436\n",
      "           pottedplant       4952        480      0.584      0.444      0.444      0.203\n",
      "                 sheep       4952        242      0.601      0.661      0.666      0.427\n",
      "                  sofa       4952        239      0.647      0.586        0.6      0.374\n",
      "                 train       4952        282      0.827      0.712      0.785      0.467\n",
      "             tvmonitor       4952        308      0.736      0.584      0.647      0.409\n",
      "Speed: 0.1ms pre-process, 1.5ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp356\u001b[0m\n",
      "Voc\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/val_kitti.yaml, weights=['runs/train/replay_Lwf_with_head_openimages/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_openimages summary: 169 layers, 7220503 parameters, 0 gradients, 64.6 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198       0.85      0.713      0.799      0.499\n",
      "                   car       2244       8711      0.884      0.871      0.931      0.677\n",
      "                   van       2244        861      0.871      0.732      0.842      0.578\n",
      "                 truck       2244        333      0.908      0.859      0.923      0.665\n",
      "                  tram       2244        138      0.875      0.891      0.944      0.597\n",
      "                person       2244       1286      0.825      0.645      0.722       0.37\n",
      "        person_sitting       2244         89      0.678      0.438      0.533      0.262\n",
      "               cyclist       2244        496      0.881      0.643      0.773      0.395\n",
      "                  misc       2244        284      0.879      0.623      0.722      0.448\n",
      "Speed: 0.0ms pre-process, 1.1ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp357\u001b[0m\n",
      "kitti\n"
     ]
    }
   ],
   "source": [
    "# 1e-4 1e-3\n",
    "model = f'runs/train/replay_Lwf_with_head_openimages/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/openimages.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'openimages' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n",
    "# Voc\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/val_VOC.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Voc' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# kitti\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/val_kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'kitti' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "553772a5-f0f0-4ced-94f3-51e62ff53c4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f819c3ff-b878-4c0c-94c2-914c1c22a54f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0303f93-6b34-407f-aff0-f3b8cd43c7b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "079cd3e7-8992-4f8a-88a7-9624c917f227",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_DER: \u001b[0mweights=./runs/train/k_v_o_replay_DER/weights/last.pt, cfg=models/yolov5s_openimages.yaml, data=data/VisDrone_incremental.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=replay_Lwf_with_head_vis, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[0.0001, 0.0001, 0.0005], Lwf_temperature=1.0, Old_models=['./runs/train/fog_02/weights/last.pt', './runs/train/increment_VOC_plain/weights/last.pt', './runs/train/k_v_o_replay_DER/weights/last.pt'], DER_enable=True, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2900 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/bd21438f167c496f8c225f164608c014\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "首次创建 extractors\n",
      "成功拼接 extractors\n",
      "extractors共有模型个数： 1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1    110583  models.yolo_DerTest.Detect              [36, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "已知类别： 0\n",
      "YOLOv5s_openimages summary: 226 layers, 7230007 parameters, 7230007 gradients, 65.3 GFLOPs\n",
      "\n",
      "Transferred 705/723 items from runs/train/k_v_o_replay_DER/weights/last.pt\n",
      "Overriding model.yaml nc=36 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_DerTest.Detect              [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_openimages summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "Overriding model.yaml nc=36 with nc=26\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_DerTest.Detect              [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_openimages summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1    110583  models.yolo_DerTest.Detect              [36, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_openimages summary: 217 layers, 7116727 parameters, 7116727 gradients, 16.2 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 75 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/labels\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/0000137_02220_d_0000163.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/0000140_00118_d_0000002.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/9999945_00000_d_0000114.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/9999987_00000_d_0000049.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-val/labels.cac\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m2.95 anchors/target, 0.933 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve...\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mWARNING ⚠️ Extremely small objects found: 29644 of 343201 labels are <3 pixels in size\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mRunning kmeans for 9 anchors on 342304 points...\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7493: 100%|████\u001b[0m\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mthr=0.25: 0.9995 best possible recall, 5.74 anchors past thr\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mn=9, img_size=640, metric_all=0.364/0.748-mean/best, past_thr=0.485-mean: 3,5, 4,9, 8,7, 8,15, 16,9, 16,21, 33,17, 29,37, 61,63\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mDone ✅ (optional: update model *.yaml to use these anchors in the future)\n",
      "Plotting labels to runs/train/replay_Lwf_with_head_vis/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/replay_Lwf_with_head_vis\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.74G    0.03751    0.03622    0.02043        431        640: 1\n",
      "tensor([11.54702], device='cuda:0', grad_fn=<AddBackward0>) tensor(22338.57812, device='cuda:0', grad_fn=<AddBackward0>), tensor(36180.23438, device='cuda:0', grad_fn=<AddBackward0>), tensor(2775.53369, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759     0.0323      0.121     0.0398     0.0152\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      3.94G    0.03145    0.04398     0.0175        589        640: 1\n",
      "tensor([11.91147], device='cuda:0', grad_fn=<AddBackward0>) tensor(21927.57227, device='cuda:0', grad_fn=<AddBackward0>), tensor(34467.61328, device='cuda:0', grad_fn=<AddBackward0>), tensor(2724.99341, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759     0.0567      0.193     0.0761     0.0271\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      3.94G    0.03004    0.04562    0.01646        586        640: 1\n",
      "tensor([12.06463], device='cuda:0', grad_fn=<AddBackward0>) tensor(21250.11328, device='cuda:0', grad_fn=<AddBackward0>), tensor(34663.85156, device='cuda:0', grad_fn=<AddBackward0>), tensor(2946.40649, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759     0.0638      0.209     0.0896     0.0386\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      3.94G    0.02975    0.04624    0.01634        785        640: 1\n",
      "tensor([11.53464], device='cuda:0', grad_fn=<AddBackward0>) tensor(20178.66016, device='cuda:0', grad_fn=<AddBackward0>), tensor(32805.04297, device='cuda:0', grad_fn=<AddBackward0>), tensor(3054.78735, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759     0.0725      0.236      0.105     0.0479\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      3.94G     0.0288    0.04594    0.01591        417        640: 1\n",
      "tensor([11.06174], device='cuda:0', grad_fn=<AddBackward0>) tensor(20032.05859, device='cuda:0', grad_fn=<AddBackward0>), tensor(32384.29297, device='cuda:0', grad_fn=<AddBackward0>), tensor(2972.48022, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759     0.0786      0.245      0.108     0.0502\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      3.94G     0.0288    0.04603    0.01593        276        640: 1\n",
      "tensor([10.12416], device='cuda:0', grad_fn=<AddBackward0>) tensor(19846.56250, device='cuda:0', grad_fn=<AddBackward0>), tensor(31571.96680, device='cuda:0', grad_fn=<AddBackward0>), tensor(2993.18994, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759     0.0898      0.264      0.119      0.055\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      3.94G    0.02844    0.04558    0.01561        436        640: 1\n",
      "tensor([10.74947], device='cuda:0', grad_fn=<AddBackward0>) tensor(18092.49023, device='cuda:0', grad_fn=<AddBackward0>), tensor(32459.26953, device='cuda:0', grad_fn=<AddBackward0>), tensor(3019.97925, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.233      0.248      0.136     0.0653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      3.94G    0.02807     0.0453     0.0153        521        640: 1\n",
      "tensor([10.87695], device='cuda:0', grad_fn=<AddBackward0>) tensor(18233.42578, device='cuda:0', grad_fn=<AddBackward0>), tensor(32120.83008, device='cuda:0', grad_fn=<AddBackward0>), tensor(3027.76733, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.382      0.206      0.145     0.0701\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      3.94G    0.02814    0.04559    0.01539        326        640: 1\n",
      "tensor([10.14886], device='cuda:0', grad_fn=<AddBackward0>) tensor(19613.11328, device='cuda:0', grad_fn=<AddBackward0>), tensor(31131.49609, device='cuda:0', grad_fn=<AddBackward0>), tensor(2930.25586, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.299      0.224      0.151     0.0705\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      3.94G    0.02797    0.04543    0.01521        498        640: 1\n",
      "tensor([10.66814], device='cuda:0', grad_fn=<AddBackward0>) tensor(19861.51562, device='cuda:0', grad_fn=<AddBackward0>), tensor(31009.69531, device='cuda:0', grad_fn=<AddBackward0>), tensor(2912.58813, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759        0.4      0.212      0.164     0.0792\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      3.94G    0.02776    0.04467    0.01498        502        640: 1\n",
      "tensor([10.43996], device='cuda:0', grad_fn=<AddBackward0>) tensor(18488.12305, device='cuda:0', grad_fn=<AddBackward0>), tensor(31303.81055, device='cuda:0', grad_fn=<AddBackward0>), tensor(2988.42188, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759       0.35      0.203      0.164     0.0786\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      3.94G    0.02752    0.04518     0.0149        568        640: 1\n",
      "tensor([11.16141], device='cuda:0', grad_fn=<AddBackward0>) tensor(19630.85352, device='cuda:0', grad_fn=<AddBackward0>), tensor(32093.87109, device='cuda:0', grad_fn=<AddBackward0>), tensor(2934.09302, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.327      0.209      0.177     0.0855\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      3.94G    0.02734    0.04497    0.01474        572        640: 1\n",
      "tensor([11.29809], device='cuda:0', grad_fn=<AddBackward0>) tensor(20216.26953, device='cuda:0', grad_fn=<AddBackward0>), tensor(31870.16602, device='cuda:0', grad_fn=<AddBackward0>), tensor(3052.31421, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.328       0.21       0.18     0.0893\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      3.94G    0.02763    0.04515    0.01495        560        640: 1\n",
      "tensor([11.29044], device='cuda:0', grad_fn=<AddBackward0>) tensor(19761.07422, device='cuda:0', grad_fn=<AddBackward0>), tensor(31867.48633, device='cuda:0', grad_fn=<AddBackward0>), tensor(2941.38452, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.338      0.206      0.185     0.0936\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      3.94G    0.02709    0.04476    0.01465        466        640: 1\n",
      "tensor([10.84842], device='cuda:0', grad_fn=<AddBackward0>) tensor(20160.08008, device='cuda:0', grad_fn=<AddBackward0>), tensor(31517.51367, device='cuda:0', grad_fn=<AddBackward0>), tensor(3069.65210, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.349      0.213      0.189     0.0944\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      3.94G    0.02737    0.04484    0.01477        709        640: 1\n",
      "tensor([11.48017], device='cuda:0', grad_fn=<AddBackward0>) tensor(19623.08008, device='cuda:0', grad_fn=<AddBackward0>), tensor(31344.80664, device='cuda:0', grad_fn=<AddBackward0>), tensor(2989.12622, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759       0.38      0.218       0.19     0.0967\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      3.94G    0.02708    0.04451    0.01458        440        640: 1\n",
      "tensor([10.43769], device='cuda:0', grad_fn=<AddBackward0>) tensor(19722.99609, device='cuda:0', grad_fn=<AddBackward0>), tensor(30846.23047, device='cuda:0', grad_fn=<AddBackward0>), tensor(2914.69629, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.281      0.221       0.19     0.0948\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      3.94G    0.02674    0.04417    0.01438        580        640: 1\n",
      "tensor([10.51220], device='cuda:0', grad_fn=<AddBackward0>) tensor(19161.75391, device='cuda:0', grad_fn=<AddBackward0>), tensor(30918.17383, device='cuda:0', grad_fn=<AddBackward0>), tensor(2930.80518, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.368      0.207      0.193     0.0982\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      3.94G    0.02711    0.04474    0.01467        503        640: 1\n",
      "tensor([10.47463], device='cuda:0', grad_fn=<AddBackward0>) tensor(18700.69922, device='cuda:0', grad_fn=<AddBackward0>), tensor(30481.01172, device='cuda:0', grad_fn=<AddBackward0>), tensor(2918.98975, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.352      0.222      0.201      0.102\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      3.94G    0.02669     0.0443    0.01438        426        640: 1\n",
      "tensor([10.29685], device='cuda:0', grad_fn=<AddBackward0>) tensor(20064.80273, device='cuda:0', grad_fn=<AddBackward0>), tensor(31253.99023, device='cuda:0', grad_fn=<AddBackward0>), tensor(2881.54858, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.316      0.222      0.205      0.105\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      3.94G    0.02713    0.04429    0.01458        705        640: 1\n",
      "tensor([10.57827], device='cuda:0', grad_fn=<AddBackward0>) tensor(18860.91797, device='cuda:0', grad_fn=<AddBackward0>), tensor(30520.22266, device='cuda:0', grad_fn=<AddBackward0>), tensor(3067.61865, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.317      0.214      0.205      0.104\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      3.94G    0.02704    0.04418    0.01457        907        640: 1\n",
      "tensor([11.90848], device='cuda:0', grad_fn=<AddBackward0>) tensor(19659.28320, device='cuda:0', grad_fn=<AddBackward0>), tensor(31537.78906, device='cuda:0', grad_fn=<AddBackward0>), tensor(3002.63184, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.369       0.22      0.211      0.108\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      3.94G    0.02699    0.04433    0.01455        591        640: 1\n",
      "tensor([11.09799], device='cuda:0', grad_fn=<AddBackward0>) tensor(19076.14453, device='cuda:0', grad_fn=<AddBackward0>), tensor(31333.86328, device='cuda:0', grad_fn=<AddBackward0>), tensor(3056.58057, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.358      0.228      0.214      0.111\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      3.94G    0.02708    0.04405    0.01454        567        640: 1\n",
      "tensor([10.44247], device='cuda:0', grad_fn=<AddBackward0>) tensor(18067.45117, device='cuda:0', grad_fn=<AddBackward0>), tensor(30611.47852, device='cuda:0', grad_fn=<AddBackward0>), tensor(2995.54517, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.341      0.227      0.215       0.11\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      3.94G    0.02696    0.04404    0.01446        519        640: 1\n",
      "tensor([10.30074], device='cuda:0', grad_fn=<AddBackward0>) tensor(18110.35352, device='cuda:0', grad_fn=<AddBackward0>), tensor(30553.73828, device='cuda:0', grad_fn=<AddBackward0>), tensor(2942.88867, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.345      0.229      0.211       0.11\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      3.94G    0.02687    0.04401    0.01446        751        640: 1\n",
      "tensor([10.98563], device='cuda:0', grad_fn=<AddBackward0>) tensor(18094.39258, device='cuda:0', grad_fn=<AddBackward0>), tensor(30662.26367, device='cuda:0', grad_fn=<AddBackward0>), tensor(3152.34619, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759       0.34      0.236      0.215      0.111\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      3.94G    0.02677    0.04393    0.01441        335        640: 1\n",
      "tensor([9.91860], device='cuda:0', grad_fn=<AddBackward0>) tensor(18826.96094, device='cuda:0', grad_fn=<AddBackward0>), tensor(31145.66406, device='cuda:0', grad_fn=<AddBackward0>), tensor(2944.97827, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.369      0.223      0.217      0.111\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      3.94G    0.02685    0.04413    0.01449        754        640: 1\n",
      "tensor([11.60626], device='cuda:0', grad_fn=<AddBackward0>) tensor(18551.69336, device='cuda:0', grad_fn=<AddBackward0>), tensor(30582.74414, device='cuda:0', grad_fn=<AddBackward0>), tensor(3034.60864, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759       0.36      0.237      0.219      0.113\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      3.94G     0.0267    0.04352    0.01431        637        640: 1\n",
      "tensor([10.68977], device='cuda:0', grad_fn=<AddBackward0>) tensor(19830.72852, device='cuda:0', grad_fn=<AddBackward0>), tensor(30613.80664, device='cuda:0', grad_fn=<AddBackward0>), tensor(2985.09082, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.378      0.238      0.224      0.116\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      3.94G    0.02635    0.04317    0.01407       1044        640: 1\n",
      "tensor([11.01823], device='cuda:0', grad_fn=<AddBackward0>) tensor(18722.17578, device='cuda:0', grad_fn=<AddBackward0>), tensor(30698.76367, device='cuda:0', grad_fn=<AddBackward0>), tensor(3083.50220, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.361       0.24      0.222      0.115\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      3.94G    0.02658    0.04354    0.01431        288        640: 1\n",
      "tensor([9.60040], device='cuda:0', grad_fn=<AddBackward0>) tensor(18350.91211, device='cuda:0', grad_fn=<AddBackward0>), tensor(30860.84961, device='cuda:0', grad_fn=<AddBackward0>), tensor(3017.12476, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.369      0.239      0.227      0.116\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      3.94G    0.02653    0.04347    0.01428        628        640: 1\n",
      "tensor([10.73746], device='cuda:0', grad_fn=<AddBackward0>) tensor(18182.14062, device='cuda:0', grad_fn=<AddBackward0>), tensor(31614.26172, device='cuda:0', grad_fn=<AddBackward0>), tensor(2973.87988, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759       0.37      0.237       0.23       0.12\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      3.94G    0.02627    0.04306    0.01402        580        640: 1\n",
      "tensor([10.84759], device='cuda:0', grad_fn=<AddBackward0>) tensor(19452.27930, device='cuda:0', grad_fn=<AddBackward0>), tensor(30306.59961, device='cuda:0', grad_fn=<AddBackward0>), tensor(2981.43652, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.381      0.237      0.232      0.121\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      3.94G    0.02619    0.04311    0.01406        746        640: 1\n",
      "tensor([11.13779], device='cuda:0', grad_fn=<AddBackward0>) tensor(19405.15625, device='cuda:0', grad_fn=<AddBackward0>), tensor(31734.86133, device='cuda:0', grad_fn=<AddBackward0>), tensor(3032.65601, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.374      0.244      0.232      0.121\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      3.94G    0.02635     0.0433    0.01418        455        640: 1\n",
      "tensor([10.16753], device='cuda:0', grad_fn=<AddBackward0>) tensor(18372.28516, device='cuda:0', grad_fn=<AddBackward0>), tensor(30982.55859, device='cuda:0', grad_fn=<AddBackward0>), tensor(2907.74390, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all        548      38759      0.374      0.244      0.232       0.12\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      3.94G    0.02635    0.04303     0.0141        505        640: 1\n",
      "tensor([10.73156], device='cuda:0', grad_fn=<AddBackward0>) tensor(19900.20898, device='cuda:0', grad_fn=<AddBackward0>), tensor(31480.64258, device='cuda:0', grad_fn=<AddBackward0>), tensor(3242.74878, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   "
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_DER.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_openimages.yaml \\\n",
    "--data data/VisDrone_incremental.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/k_v_o_replay_DER/weights/last.pt \\\n",
    "--name replay_Lwf_with_head_vis \\\n",
    "--DER_enable \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda \\\n",
    "        1e-4 \\\n",
    "        1e-4 \\\n",
    "        5e-4 \\\n",
    "--Old_models \\\n",
    "        ./runs/train/fog_02/weights/last.pt \\\n",
    "        ./runs/train/increment_VOC_plain/weights/last.pt \\\n",
    "        ./runs/train/k_v_o_replay_DER/weights/last.pt \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "#15:41:07开始16:32:53结束\n",
    "# (19 + 32) / 60 = 0.85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "001912fe-3ead-4632-8e49-a59d12acdc73",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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